Methods of detecing lung cancer

ABSTRACT

The present invention provides a method of predicting whether a pulmonary nodule in a subject is benign or non-small cell lung cancer, comprising obtaining the results of an assay that measures an expression level of miR205-5p in a plasma sample from the subject; obtaining the results of an assay that measures an expression level of miR126 in a plasma sample from the subject; obtaining the results of an assay that provides a size of the pulmonary nodule in the subject; and calculating a probability value based on the combination of the expression levels of miR205-5p and miR126, and the size of the pulmonary nodule, wherein if the probability value exceeds a specified threshold, the pulmonary nodule is predicted as non-small cell lung cancer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Appl. No.62/482,222, filed Apr. 6, 2017, the contents of which are herebyincorporated by reference in their entirety.

STATEMENT OF FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

This invention was made with government support under Grant NumberCA205746 awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ELECTRONICALLY

Incorporated by reference in its entirety herein is a computer-readablesequence listing submitted concurrently herewith and identified asfollows: One 10,570 Byte ASCII (Text) file named“Sequence_Listing_ST25.txt,” created on Apr. 5, 2018.

FIELD OF THE INVENTION

The field of the invention relates to lung cancer biology. Inparticular, the field of the invention relates to the diagnosis andprognosis thereof.

BACKGROUND OF THE INVENTION

Lung cancer is the leading cancer killer worldwide, of which more than85% are non-small cell lung cancer (NSCLC). Tobacco smoking is the majorcause of NSCLC. A NCI-National Lung Screening Trial (NLST) showed thatthe early detection of lung cancer using low-dose CT (LDCT) cansignificantly reduce mortality rates (Aberle, et al., N Engl J Med 365:395-409, 2011). LDCT is now used for lung cancer screening in smokers(Moyer V A., Ann Intern Med 160: 330-8, 2014). However, LDCT isassociated with over-diagnosis, excessive cost, and radiation exposure(Patz E F, Jr., et al., JAMA Intern Med 174: 269-74, 2014; Aberle, etal., N Engl J Med 365: 395-409, 2011). The CT scan has dramaticallyincreased the number of indeterminate pulmonary nodules (PNs) inasymptomatic individuals. 24.2% of heavy smokers had indeterminate PNsdetected by LDCT, whereas 96.4% of these PNs were ultimately confirmedas benign growths (Patz E F, Jr., et al., JAMA Intern Med 174: 269-74,2014). The development of non-invasive or circulating biomarkers thatcan accurately and cost-effectively diagnose early stage lung cancer isrequired (Hubers A J, et al., Br J Cancer 109(3): 530-537, 2013).

During tumor development, cancer cells undergo apoptosis and necrosis,and release tumor-associated molecules that can circulate in thebloodstream. The tumors-derived molecules in plasma may providecell-free circulating cancer biomarkers. Numerous plasma biomarkers havebeen developed by detecting the circulating cell-free DNA, genemethylated products, proteins, and metabolites for lung cancer earlydetection (Hubers A J, et al., Br J Cancer 109(3): 530-537, 2013).However, due to low sensitivity rates for diagnosis, none of them hasbeen well accepted in clinics (Hubers A J, et al., Br J Cancer 109(3):530-537, 2013). For instance, a blood test (Cancer-SEEK) was recentlydeveloped that could detect eight common cancer types by determiningcirculating proteins and mutations of cell-free DNA (Cohen J D, et al.,Science 359(6378): 926-930, 2018). However, the test had about 60%sensitivity for diagnosis of all stages of lung cancer and only 40%sensitivity for the stage I disease (Cohen J D, et al., Science359(6378): 926-930, 2018).

MicroRNAs (miRNAs) are small non-coding RNA molecules (containing about22 nucleotides) that function as posttranscriptional regulators of geneexpression. Dysregulation of miRNAs plays a crucial role in lungtumorigenesis (Costa F F, Gene 357: 83-94, 2005; Yanaihara N, et al.,Cancer Cell 9: 189-98, 2006; Shen J, Jiang F, Expert Opin Med Diagn 6:197-207, 2012; Mitchell P S, et al., Proc Natl Acad Sci USA 105:10513-8, 2008). Due to their small size and relative resistance tonucleases, miRNAs are highly stable in peripheral plasma which is aneasily accessible and rich biological fluid (Mitchell P S, et al., ProcNatl Acad Sci USA 105: 10513-8, 2008). Plasma miRNAs that are directlyreleased from primary lung tumors or the circulating lung cancer cellsmight provide circulating biomarkers for lung cancer (Mitchell P S, etal., Proc Natl Acad Sci USA 105: 10513-8, 2008). Numerous miRNAs havebeen identified as diverse panels of biomarkers, which, however, producewidespread inconsistent results in lung cancer diagnosis (Shen J, etal., Lab Invest 91: 579-87, 2011; Chen X, et al., Cell Res 18: 997-1006,2008). Furthermore, sensitivities and specificities of these plasmamiRNA biomarkers are not high enough to be used in the clinical settingsfor predicting malignancy among indeterminate PNs (Shen J, Jiang F,Expert Opin Med Diagn 6: 197-207, 2012).

Long non-coding RNAs (lncRNAs) have minimum transcript length of 200 bpand play vital roles in various biological processes (Ma, L., et al.,RNA Biol 10: 925-933, 2013). lncRNAs can regulate different molecularsignaling pathways via changing gene expression, and therefore, theirdysregulations are implicated in numerous mechanisms of carcinogenesis(Meseure, D., et al., Biomed Res Int 320214, 2015; Prensner, J. R., andChinnaiyan, A. M., Cancer Discov 1, 391-407, 2011). Dysregulation ofsome lncRNAs has been found in relation to oncogenesis and metastasis oflung tumor (Zhou, M., et al., J Transl Med 13, 231, 2015; Li, M., etal., Tumour Biol 36, 9969-9978, 2015; Schmidt, L., et al., J ThoracOncol 6, 1984-1992, 2011). Importantly, plasma lncRNAs directly releasedfrom primary tumors or the circulating cancer cells might providebiomarkers for human malignancies (Liang, W, et al., Medicine(Baltimore) 95, e4608, 2016).

To date, several plasma lncRNAs have been identified that show thepotential for distinguishing lung cancer patients from non-cancersubjects (Zhu, Q., et al., J Cell Mol Med 21, 2184-2198, 2017). Yet noneof them has been accepted in the clinical settings for lung cancerdiagnosis, mainly due to the low sensitivity and specificity. Recentstudies have characterized 21 lncRNAs whose aberrations are associatedwith lung cancer (Li, M., et al., Tumour Biol 36, 9969-9978, 2015).Furthermore, using whole-genomic next generation sequencing (NGS) toanalyze ncRNA profile of primary lung tumor tissues, five additionallung cancer—related lncRNAs were identified (Ma, J., et al., Mol Oncol8, 1208-1219, 2014; Gao, L., et al., Int J Cancer 136, E623-629, 2015).These lung tumor-associated lncRNAs may provide a comprehensive list ofbiomarker candidates for developing circulating lung cancer biomarkers.

Glycosylation is one of the most abundant protein modifications, andinvolved in major physiological events, including cell differentiation,proliferation, trafficking, migration and intracellular andintercellular signaling. Gathering evidences have demonstrated thataberrant glycosylation is the result of alterations inglycosyltransferases that play crucial in the development andprogression of carcinogenesis (Chachadi V B, et al., Glycobiology 25(9):963-975, 2015). Fucosylation is the major type of glycosylation andregulated by fucosyltransferases (FUTs), which catalyze the transfer ofthe fucose residue from GDP-fucose donor substrate to acceptorsubstrates present on oligosaccharides, glycoproteins and glycolipids(Wu L H, et al., Glycobiology 20(2): 215-223, 2010). Aberrantfucosylation is associated with malignant transformation.

There are 13 different FUTs, including FUT1 to 11, proteinO-fucosyltransferase 1 (POFUT1), and POFUT2 (Zhou W, et al., Oncotarget8(57): 97246-97259, 2017; Sullivan F X, et al., J Biol Chem 273(14):8193-8202, 1998). Abnormal protein expression of FUTs has been proven toassociate the development and progression of malignancies, includinglung cancer (Zhou W, et al., Oncotarget 8(57): 97246-97259, 2017;Watanabe K, et al., Surg Today 46(10): 1217-1223, 2016; Honma R, et al.,Oncology 88(5): 298-308, 2015; Noda K, et al., Hepatology 28(4):944-952, 1998). Furthermore, studying glycans and glycan-bindingproteins has shown that changes in fucosylation of glycoproteins werepotential cancer biomarkers. For example, increased fucosylatedalpha-fetoprotein (AFP) level is observed in sera of patients withhepatocellular carcinoma. AFP is one of the most representative types ofglycan-related cancer biomarkers (Breborowicz J, et al., Scand J Immunol14(1): 15-20, 1981; Aoyagi Y, et al., Cancer 67(9): 2390-2394, 1991).However, AFP is also elevated in sera of patients with various benigndiseases such as accurate and chronic hepatitis (Taniguchi N and KizukaY, Adv Cancer Res 126: 11-51, 2015). Therefore, the fucosylatedglycoproteins-based circulating biomarkers exhibit an insufficient valuein the diagnosis of malignancies.

There is a significant need for new, non-invasive screening techniquesfor detecting pulmonary nodules and lung cancer tumors. There is also asignificant need to develop techniques with greater sensitivity andspecificity than techniques currently available.

This background information is provided for informational purposes only.No admission is necessarily intended, nor should it be construed, thatany of the preceding information constitutes prior art against thepresent invention.

SUMMARY

It is to be understood that both the foregoing general description ofthe embodiments and the following detailed description are exemplary,and thus do not restrict the scope of the embodiments.

In one aspect, the invention provides a method of predicting whether apulmonary nodule in a subject is benign or non-small cell lung cancer,comprising

-   -   a. obtaining the results of an assay that measures an expression        level of miR205-5p in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of miR126 in a plasma sample from the subject;    -   c. obtaining the results of an assay that provides a size of the        pulmonary nodule in the subject; and    -   d. calculating a probability value based on the combination of        the expression levels of miR205-5p and miR126, and the size of        the pulmonary nodule,        wherein if the probability value exceeds a specified threshold,        the pulmonary nodule is predicted as non-small cell lung cancer.

In another aspect, the invention provides use of miR205-5p and miR126 asbiomarkers in combination with size of a pulmonary nodule for predictingnon-small cell lung cancer in a subject.

In another aspect, the invention provides a kit comprising one or morereagents for detection of miR205-5p and miR126 from a sample.

In another aspect, the invention provides a non-invasive method forassessing efficacy of a treatment in a subject diagnosed with non-smallcell lung cancer, comprising:

-   -   a. obtaining the results of an assay that measures an expression        level of miR205-5p in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of miR126 in a plasma sample from the subject;    -   c. obtaining the results of an assay that provides a size of the        pulmonary nodule in the subject; and    -   d. generating a receiver operating characteristic (ROC) curve        and calculating an area under the ROC curve (AUC), said area        under the curve (AUC) comprising a first comparator value;    -   e. administering a treatment for non-small cell lung cancer to        the subject;    -   f. repeating steps a) to d) to calculate a second AUC value;    -   g. comparing the second AUC value to the first comparator value;        wherein a lesser second AUC value indicates that the selected        treatment is efficacious against the non-small cell lung cancer.

In another aspect, the invention provides a method for predicting thepresence of non-small cell lung cancer in a subject, comprising

-   -   a. obtaining the results of an assay that measures an expression        level of miR-210 in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of FUT8 in a plasma sample from the subject;    -   c. obtaining the results of an assay that measures an expression        level of SNHG1;    -   d. calculating a probability value based on the combination of        the expression levels of miR-210, FUT8, and SNHG1,        wherein if the probability value exceeds a specified threshold,        the subject is predicted to have lung cancer.

In another aspect, the invention provides use of the combination ofmiR210, FUT8 and SNHG1 for predicting lung cancer in a subject.

In another aspect, the invention provides a kit comprising one or morereagents for detection of miR210, FUT8 and SNHG1 from a sample.

In another aspect, the invention provides a non-invasive method forassessing efficacy of a treatment in a subject diagnosed with lungcancer, comprising:

-   -   a. obtaining the results of an assay that measures an expression        level of miR210 in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of FUT8 in a plasma sample from the subject;    -   c. obtaining the results of an assay that measures an expression        level of SNHG1;    -   d. generating a receiver operating characteristic (ROC) curve        and calculating an area under the ROC curve (AUC), said area        under the curve (AUC) comprising a first comparator value;    -   e. administering a treatment for lung cancer to the subject;    -   f. repeating steps a) to d) to calculate a second AUC value;    -   g. comparing the second AUC value to the first comparator value;        wherein a lesser second AUC value indicates that the treatment        is efficacious against lung cancer.

Other objects, features and advantages of the present invention willbecome apparent from the following detailed description. It should beunderstood, however, that the detailed description and the specificexamples, while indicating specific embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE FIGURES

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. The drawings are not intended tolimit the scope of the present teachings in any way.

FIG. 1 . Receiver-operator characteristic (ROC) curve analysis of theclassifier, panel of the three plasma miRNA biomarkers, and Mayo Clinicmodel for distinguishing malignant from benign PNs in a training set ofpatients. The area under the ROC curve (AUC) for each approach conveysits accuracy for diagnosis of malignant PNs. The classifier (probabilityvalue=8687+1.5172×log(copy number of miR205-5p/μl plasmasample)−2.5117×log(copy number of miR-126/μl plasmasample)+0.8262×diameter of pulmonary nodule in centimeters) produces ahigher AUC value (B) for identifying malignant PNs, compared with thepanel of the three plasma miRNA biomarkers (miRs-126, 210, and 205-5p)(A) and Mayo Clinic model (C) (All P<0.05).

FIG. 2 . Expression levels of SNHG1 and RMRP in plasma samples of 63lung cancer patients and 33 cancer-free controls. (A), SNHG1 and RMRPdisplayed a higher plasma level in lung cancer patients vs. cancer-freecontrols (all p<0.001). (B), the receiver operating characteristic (ROC)curves of SNHG1 and RMRP produced an area under the ROC curve (AUC) of0.90 and 0.80, respectively, in diagnosis of lung cancer.

FIG. 3 . mRNA expression levels of 13 Futs in surgical lung tumortissues and the paired noncancerous lung tissues. Four genes (Futs-4, 7and 8, and pofut1) show a different level in lung tumor tissues ascompared with the paired noncancerous lung tissues (All p<0.05).

FIG. 4 . mRNA expression levels of Fut8 and Pofut1 in plasma samples of64 lung cancer patients and 32 cancer-free controls. (A), Fut8 andPofut1 displayed a higher plasma level in lung cancer patients vs.cancer-free controls (all p<0.0001). (B), the receiver operatingcharacteristic (ROC) curves of Fut8 and Pofut1 produced an area underthe ROC curve (AUC) of 0.86 and 0.81, respectively, in diagnosis of lungcancer.

FIG. 5 . Receiver-operator characteristic (ROC) curve analysis of the 2combined plasma lncRNAs (SNHG1 and RMRP). The area under the ROC curve(AUC) conveys its accuracy for diagnosis of malignant PNs.

FIG. 6 . Identification of a panel of 2 glycosylation genes as novelplasma biomarkers for lung cancer. (A) Copies of FUTs/μL for 13 FUTscomparing normal lung tissues and lung tumor tissues. (B) Comparison ofFUT8 in plasma with tissues. (C) Receiver-operator characteristic (ROC)curve analysis of 2 plasma FUT genes (FUT8 and Pofut1). The area underthe ROC curve (AUC) conveys its accuracy for diagnosis of malignant PNs.

FIG. 7 . Receiver-operator characteristic (ROC) curve analysis of the3-integromic plasma markers (SNHG1, FUT8, and miR-210). The area underthe ROC curve (AUC) conveys its accuracy for diagnosis of malignant PNs.From the marker panels, 3 genes were selected as a 3-integromic plasmamarker signature: Probability of a lung cancerpatient=−7.29+2.8*log(SNHG1)+3.83*log(FUT8)+3.36*log(miR-210).

FIG. 8 . Microarray analysis showed that 11 miRNAs displayed asignificantly different level in plasma samples of patients withmalignant PNs versus individuals with benign diseases.

DETAILED DESCRIPTION OF THE INVENTION

It is shown herein that plasma expression levels of miR205-5p and miR126in combination with pulmonary nodule size can be useful to predictwhether a pulmonary nodule is benign or is non-small cell lung cancer.It is also shown herein that plasma expression levels of miR-210, FUT8,and the long non-coding RNA (lncRNA) SNHG1 can be useful to predictwhether a subject, such as a heavy smoker, has lung cancer.

Reference will now be made in detail to the presently preferredembodiments of the invention which, together with the drawings and thefollowing examples, serve to explain the principles of the invention.These embodiments describe in sufficient detail to enable those skilledin the art to practice the invention, and it is understood that otherembodiments may be utilized, and that structural, biological, andchemical changes may be made without departing from the spirit and scopeof the present invention. Unless defined otherwise, all technical andscientific terms used herein have the same meanings as commonlyunderstood by one of ordinary skill in the art.

In some embodiments, the practice of the present invention employsvarious techniques of molecular biology (including recombinanttechniques), microbiology, cell biology, biochemistry and immunology.See, e.g., Sambrook et al. Molecular Cloning: A Laboratory Manual,2^(nd) edition (1989); Current Protocols in Molecular Biology (F. M.Ausubel et al. eds. (1987)); the series Methods in Enzymology (AcademicPress, Inc.); PCR: A Practical Approach (M. MacPherson et al. IRL Pressat Oxford University Press (1991)); PCR 2: A Practical Approach (M. J.MacPherson, B. D. Hames and G. R. Taylor eds. (1995)); Antibodies, ALaboratory Manual (Harlow and Lane eds. (1988)); Using Antibodies, ALaboratory Manual (Harlow and Lane eds. (1999)); and Animal Cell Culture(R. I. Freshney ed. (1987)).

Definitions of common terms in molecular biology may be found, forexample, in Benjamin Lewin, Genes VII, published by Oxford UniversityPress, 2000 (ISBN 019879276X); Kendrew et al. (eds.); The Encyclopediaof Molecular Biology, published by Blackwell Publishers, 1994 (ISBN0632021829); and Robert A. Meyers (ed.), Molecular Biology andBiotechnology: a Comprehensive Desk Reference, published by Wiley, John& Sons, Inc., 1995 (ISBN 0471186341).

For the purpose of interpreting this specification, the followingdefinitions will apply and whenever appropriate, terms used in thesingular will also include the plural and vice versa. In the event thatany definition set forth below conflicts with the usage of that word inany other document, including any document incorporated herein byreference, the definition set forth below shall always control forpurposes of interpreting this specification and its associated claimsunless a contrary meaning is clearly intended (for example in thedocument where the term is originally used). The use of “or” means“and/or” unless stated otherwise. As used in the specification andclaims, the singular form “a,” “an” and “the” include plural referencesunless the context clearly dictates otherwise. For example, the term “anantibody” includes a plurality of antibodies, including mixturesthereof. The use of “comprise,” “comprises,” “comprising,” “include,”“includes,” and “including” are interchangeable and not intended to belimiting. Furthermore, where the description of one or more embodimentsuses the term “comprising,” those skilled in the art would understandthat, in some specific instances, the embodiment or embodiments can bealternatively described using the language “consisting essentially of”and/or “consisting of.”

As used herein, the term “about” means plus or minus 10% of thenumerical value of the number with which it is being used.

In one embodiment, the invention provides for the use of miR205-5p andmiR126 as biomarkers in combination with size of a pulmonary nodule forpredicting and/or diagnosing lung cancer in a subject. The presentinvention also provides a method for determining a prognosis of a lungcancer patient. In some embodiments, the size of the pulmonary noduleand the markers miR205-5p and miR126 are quantified in a plasma sampleobtained after a subject is diagnosed with lung cancer. Periodicanalysis of the biomarkers and nodule size in the subject are useful indetermining the aggressiveness of an identified cancer as well as itslikelihood of responding to a given treatment.

In another embodiment, the invention provides for the use of acombination of miR210, FUT8 and lncRNA (SNHG1) for predicting and/ordiagnosing non-small cell lung cancer in a subject. The presentinvention also provides a method for determining a prognosis of a lungcancer patient. In some embodiments, the markers miR210, FUT8 and lncRNA(SNHG1) are quantified in a plasma sample obtained after a subject isdiagnosed with lung cancer. Periodic analysis of the biomarkers andnodule size in the subject are useful in determining the aggressivenessof an identified cancer as well as its likelihood of responding to agiven treatment.

In another embodiment, the invention provides a method of predictingwhether a pulmonary nodule in a subject is benign or non-small cell lungcancer, comprising

-   -   a. obtaining the results of an assay that measures an expression        level of miR205-5p in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of miR126 in a plasma sample from the subject;    -   c. obtaining the results of an assay that provides a size of the        pulmonary nodule in the subject; and    -   d. calculating a probability value based on the combination of        the expression levels of miR205-5p and miR126, and the size of        the pulmonary nodule,        wherein if the probability value exceeds a specified threshold,        the pulmonary nodule is predicted as non-small cell lung cancer.

In another embodiment, the present invention also provides for a methodof identifying a subject as having a poor prognosis for non-small celllung cancer, the method comprising

-   -   a. obtaining the results of an assay that measures an expression        level of miR205-5p in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of miR126 in a plasma sample from the subject;    -   c. obtaining the results of an assay that provides a size of the        pulmonary nodule in the subject;    -   d. calculating a first probability value based on the        combination of the expression levels of miR205-5p and miR126,        and the size of the pulmonary nodule;    -   e. repeating steps a-c. after a period of time and calculating a        second probability value based on the combination of the        expression levels of miR205-5p and miR126, and the size of the        pulmonary nodule; and    -   f. comparing the first and second probability values,        wherein if the second probability value is greater than the        first probability value, the subject has a poor prognosis for        non-small cell lung cancer.

In another embodiment, the invention provides a non-invasive method forassessing efficacy of a treatment in a subject diagnosed with non-smallcell lung cancer, comprising:

-   -   a. obtaining the results of an assay that measures an expression        level of miR205-5p in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of miR126 in a plasma sample from the subject;    -   c. obtaining the results of an assay that provides a size of the        pulmonary nodule in the subject; and    -   d. generating a receiver operating characteristic (ROC) curve        and calculating an area under the ROC curve (AUC), said area        under the curve (AUC) comprising a first comparator value;    -   e. administering a treatment for non-small cell lung cancer to        the subject;    -   f. repeating steps a) to d) to calculate a second AUC value;    -   g. comparing the second AUC value to the first comparator value;        wherein a lesser second AUC value indicates that the selected        treatment is efficacious against the non-small cell lung cancer.

In another embodiment, the invention provides a method of detectingmiR205-5p and miR126 in a subject, comprising

-   -   a. obtaining a plasma sample from the subject; and    -   b. detecting whether miR205-5p and miR126 are present in the        sample by measuring the expression level of miR205-5p and miR126        in the plasma sample.

In another embodiment, the invention provides a method for predictingthe presence of non-small cell lung cancer in a subject, comprising

-   -   a. obtaining the results of an assay that measures an expression        level of miR-210 in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of FUT8 in a plasma sample from the subject;    -   c. obtaining the results of an assay that measures an expression        level of SNHG1;    -   d. calculating a probability value based on the combination of        the expression levels of miR-210, FUT8, and SNHG1,        wherein if the probability value exceeds a specified threshold,        the subject is predicted to have lung cancer.

In another embodiment, the present invention also provides for a methodof identifying a subject as having a poor prognosis for non-small celllung cancer, the method comprising

-   -   b. obtaining the results of an assay that measures an expression        level of miR-210 in a plasma sample from the subject;    -   c. obtaining the results of an assay that measures an expression        level of FUT8 in a plasma sample from the subject;    -   d. obtaining the results of an assay that measures an expression        level of SNHG1;    -   e. calculating a first probability value based on the        combination of the expression levels of miR-210, FUT8, and        SNHG1;    -   f. repeating steps a-c. after a period of time and calculating a        second probability value based on the combination of the        expression levels of miR-210, FUT8, and SNHG1; and    -   g. comparing the first and second probability values        wherein if the second probability value is greater than the        first probability value, the subject has a poor prognosis for        lung cancer.

In another embodiment, the invention provides a non-invasive method forassessing efficacy of a treatment in a subject diagnosed with non-smallcell lung cancer comprising:

-   -   a. obtaining the results of an assay that measures an expression        level of miR210 in a plasma sample from the subject;    -   b. obtaining the results of an assay that measures an expression        level of FUT8 in a plasma sample from the subject;    -   c. obtaining the results of an assay that measures an expression        level of SNHG1;    -   d. generating a receiver operating characteristic (ROC) curve        and calculating an area under the ROC curve (AUC), said area        under the curve (AUC) comprising a first comparator value;    -   e. administering a treatment for lung cancer to the subject;    -   f. repeating steps a) to d) to calculate a second AUC value;    -   g. comparing the second AUC value to the first comparator value;        wherein a lesser second AUC value indicates that the treatment        is efficacious against lung cancer.

In another embodiment, the invention provides a method of detectingmiR210, FUT8, and SNHG1 in a subject, comprising

-   -   a. obtaining a plasma sample from the subject; and    -   b. detecting whether miR210, FUT8, and SNHG1 are present in the        sample by measuring the expression level of miR210, FUT8, and        SNHG1 in the plasma sample.

In some embodiments, the pulmonary nodule that is non-small cell lungcancer is an adenocarcinoma, squamous cell carcinoma or large cellcarcinoma.

In some embodiments, the methods and uses as described herein arenon-invasive and demonstrate an improvement in early detection overtraditional methods such as low-dose computer tomography (LDCT). In someembodiments, only a plasma sample is required from a subject of interestto assay for expression levels of the polynucleotide markers describedherein, although other biological fluids are contemplated.

In some embodiments, methods as described herein enable early detection.In some embodiments, the biomarkers function as an easy-to-perform assayfor expression levels at a first screening to pre-identify subjects,such as smokers, for lung cancer. Subsequently screening thepre-identified individuals using CT imaging is costly way to diagnoselung cancer. Using the plasma biomarkers and pulmonary nodule sizecharacteristics for specifically identifying lung cancer in a CTscreening positive setting reduces the lung cancer-related mortality byi), sparing smokers with benign pulmonary nodules from the invasivebiopsies and expensive follow-up examinations, ii) improving CT forprecisely and preoperatively identifying lung cancer, and iii)facilitating effective treatments to be instantly initiated for lungcancer. As such, the methods of the invention are useful for riskassessment. The methods of the invention enable a quantitative,probabilistic method to determine when subjects, such as heavy smokersare predisposed to lung cancer. In some embodiments, the methods furthercomprise treating the cancer in the subject.

In some embodiments, calculating the probability of lung cancercomprises generating a receiver operating characteristic (ROC) curve;and calculating an area under the ROC curve (AUC), where the area underthe curve (AUC) provides the probability of lung cancer in the subject.In some embodiments, the minimum statistically determined value of theprobability is at least 80%. In some embodiments, the minimumstatistically determined value of the probability is at least 0.80, atleast 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94,at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least0.99.

In accordance with the methods of the invention, the expression levelsof miR205-5p, miR126, miR-210, FUT8, and SNHG1 are determined in plasmasamples. The methods of determining expression levels are notparticularly limiting. In some embodiments, the expression levels aredetermined by quantitative RT-PCR. In some embodiments, the expressionlevels are determined by microarray or by a Northern blot. In someembodiments, the expression levels are determined by droplet digitalPCR. In some embodiments, the methods comprise determining an absoluteexpression level in a sample without the need of a control, such as aninternal control gene. In some embodiments, the methods comprisedetermining the expression levels of 1) miR205-5p and miR126; and/or 2)miR-210, FUT8, and SNHG1 alone, wherein the expression levels of otherpolynucleotides are not determined or assayed. In some embodiments,expression levels are not compared with expression levels in controlplasma samples. In some embodiments, the expression levels are comparedto a control plasma sample. In particular embodiments, the controlsample is derived from a healthy subject. In other particularembodiments, the control sample is derived from the same subject at anearlier point in time.

As used herein, the term “subject” includes both human and animalsubjects. In some embodiments, the term “subject” includes a human orother animal at risk of developing lung cancer or suffering from lungcancer. A subject can be, but is not limited to, a lung cancer patient,an undiagnosed smoker or other undiagnosed subject presenting with oneor more symptoms associated with lung cancer or having pulmonary nodulesnot yet diagnosed as malignant. It also includes individuals who do nothave lung cancer. Non-limiting examples of animal subjects include cats,dogs, rats, mice, swine, and primates. In some embodiments, the subjectis a smoker, former smoker or non-smoker exposed to second hand smoke.In some embodiments, the subject has a smoking history selected from thegroup consisting of at least 15 pack-years, at least 20 pack-years, atleast 25 pack-years at least 30 pack-years, at least 35 pack-years, atleast 40 pack-years, at least 45 pack-years, at least 50 pack-years, atleast 55 pack-years, at least 60 pack-years, and at least 65 pack-years.In some embodiments, the subject is at least 35 years old, at least 40years old, at least 45 years old, at least 50 years old, at least 55years old, at least 60 years old, or at least 65 years old. In someembodiments, the subject is between 55 and 80 years old. In someembodiments, the subject is between 55 and 80 years old and has asmoking history of at least 35 pack-years.

In some embodiments, the plasma sample is assayed for contaminatingmiRNA, that might arise, e.g., from blood cells. In some embodiments,the contaminating miRNA is selected from the group consisting ofRBC-related miRNA (mir-451), myeloid-related miRNA (miR-223),lymphoid-associated miRNA (miR-150) and combinations thereof.

In some embodiments, miR-126 comprises SEQ ID NO:1 (NCBI ReferenceSequence: NR_029695.1). In some embodiments, the expression level ofmiR-126 is assayed using 5′-TCGTACCGTGAGTAATAATGCG-3′ (SEQ ID NO:6) asthe forward primer and mRQ 3′ primer (Clontech, Mountain View, Calif.)as the reverse primer.

In some embodiments, miR-205-5p comprises SEQ ID NO:2 (NCBI ReferenceSequence: NR_029622.1). In some embodiments, the expression level ofmiR-205-5p is assayed using 5′-TCCTTCATTCCACCGGAGTCTG-3′ (SEQ ID NO:7)as the forward primer and mRQ 3′ primer (Clontech, Mountain View,Calif.) as the reverse primer.

In some embodiments, miR-210 comprises SEQ ID NO:3 (NCBI ReferenceSequence: NR_029623.1). In some embodiments, the expression level ofmiR-210 is assayed using 5′-CTGTGCGTGTGACAGCGGCTGA-3′ (SEQ ID NO:8) asthe forward primer and mRQ 3′ primer (Clontech, Mountain View, Calif.)as the reverse primer.

FUT8 corresponds to fucosyltransferase 8 polynucleotide sequence. Insome embodiments, FUT8 comprises SEQ ID NO:4 (NCBI Reference Sequence:NM_178155.2 (2558-2655) 98 bp). In some embodiments, the expressionlevel of FUT8 corresponding to nucleotides 2558-2655 of SEQ ID NO:4 isassayed using 5′-GTCAGGTGAAGTGAAGGACAA-3′ (SEQ ID NO:9) as the forwardprimer and 5′-CTGGTACAGCCAAGGGTAAAT-3′ (SEQ ID NO:10) as the reverseprimer.

In some embodiments, SNHG1 comprises SEQ ID NO:5 (NCBI ReferenceSequence: NR_003098.1 (267-354) 88 bp). In some embodiments, theexpression level of SNHG1 corresponding to nucleotides 267-354 of SEQ IDNO:5 is assayed using 5′-CCTTCAGAGCTGAGAGGTACTA-3′ (SEQ ID NO:11) as theforward primer and 5′-CTCAAACTCCTCTTGGGCTTTA-3′ (SEQ ID NO:12) as thereverse primer.

In some embodiments, the probability value in the method of predictingwhether a pulmonary nodule in a subject is benign or non-small cell lungcancer is calculated by a classifier having the following formula:

probability value=8687+1.5172×log(copy number of miR205-5p/μl plasmasample)−2.5117×log(copy number of miR-126/μl plasmasample)+0.8262×diameter of pulmonary nodule in centimeters. In someembodiments, the classifier yields about 90% sensitivity and about 90%specificity for diagnosis of non-small cell lung cancer.

In some embodiments, if the probability value exceeds 0.85, thepulmonary nodule is predicted as non-small cell lung cancer. In someembodiments, if the probability value exceeds 0.90, the pulmonary noduleis predicted as non-small cell lung cancer. In some embodiments, if theprobability value exceeds 0.95, the pulmonary nodule is predicted asnon-small cell lung cancer. In some embodiments, if the probabilityvalue exceeds 0.99, the pulmonary nodule is predicted as non-small celllung cancer.

In some embodiments, the probability value in the method of predictingthe presence of lung cancer in a subject is calculated by a classifierhaving the following formula:

probability value=−7.29+2.8×log(copy number of SNHG1/μl plasmasample)+3.83×log(copy number of FUT8/μl plasma sample)+3.36×log(copynumber of miR-210/μl plasma sample). In some embodiments, the classifieryields about 95% sensitivity and about 95% specificity for diagnosis oflung cancer.

In some embodiments, if the probability value exceeds 0.80, the presenceof lung cancer is predicted in the subject. In some embodiments, if theprobability value exceeds 0.90, the presence of lung cancer is predictedin the subject. In some embodiments, if the probability value exceeds0.95, the presence of lung cancer is predicted in the subject. In someembodiments, if the probability value exceeds 0.99, the presence of lungcancer is predicted in the subject.

In the above formulas, copy number refers to the number of moleculespresent in the sample volume.

In some embodiments, if the subject is diagnosed with lung cancer ornon-small cell lung cancer, the methods further comprise treating thecancer. In some embodiments, the treatment is selected fromadministration of a therapeutic agent, surgery, radiofrequency ablation,radiation, and combinations thereof.

As used herein, the terms “treatment” or “treating” relate to anytreatment of a condition of interest (e.g., lung cancer), including butnot limited to therapeutic treatment, which can include inhibiting theprogression of a condition of interest; arresting or preventing thefurther development of a condition of interest; reducing the severity ofa condition of interest; ameliorating or relieving symptoms associatedwith a condition of interest; and causing a regression of a condition ofinterest or one or more of the symptoms associated with a condition ofinterest.

Therapeutic agents used in accordance with the invention are typicallyadministered in an effective amount to achieve the desired response. Ofcourse, the effective amount in any particular case will depend upon avariety of factors including the activity of the therapeuticcomposition, formulation, the route of administration, combination withother drugs or treatments, severity of the condition being treated, andthe physical condition and prior medical history of the subject beingtreated. In some embodiments, a minimal dose is administered, and thedose is escalated in the absence of dose-limiting toxicity to aminimally effective amount. Determination and adjustment of atherapeutically effective dose, as well as evaluation of when and how tomake such adjustments, are known to those of ordinary skill in the art.

A dosing schedule may be varied on a patient by patient basis, takinginto account, for example, factors such as the weight and age of thepatient, the type of disease being treated, the severity of the diseasecondition, previous or concurrent therapeutic interventions, the mannerof administration and the like, which can be readily determined by oneof ordinary skill in the art. In some embodiments, the therapeutic agentis administered in a dose of between about 0.01 mg/kg to about 100 mg/kgbody weight. In some embodiments, the dose administered is about 1-50mg/kg body weight of the subject. In some embodiments, the doseadministered is about 5-25 mg/kg body weight of the subject. In someembodiments, the dose administered is about 10 mg/kg body weight of thesubject.

In some embodiments, the therapeutic agent is a chemotherapeutic agent.In some embodiments, the chemotherapeutic agent is selected from thegroup consisting of cisplatin, carboplatin, paclitaxel (Taxol),albumin-bound paclitaxel (nab-paclitaxel, Abraxane), docetaxel(Taxotere), gemcitabine (Gemzar), vinorelbine (Navelbine), irinotecan(Camptosar), etoposide (VP-16), vinblastine, pemetrexed (Alimta), andcombinations thereof.

In some embodiments, the therapeutic agent is selected from the groupconsisting of bevacizumab (Avastin), ramucirumab (Cyramza), erlotinib(Tarceva), afatinib (Gilotrif), gefitinib (Iressa), osimertinib(Tagrisso), necitumumab (Portrazza), crizotinib (Xalkori), ceritinib(Zykadia), alectinib (Alecensa), brigatinib (Alunbrig), dabrafenib(Tafinlar), trametinib (Mekinist), nivolumab (Opdivo), pembrolizumab(Keytruda), atezolizumab (Tecentriq), durvalumab (Imfinzi), andcombinations thereof.

In some embodiments, the radiation is selected from the group consistingof external beam radiation therapy and brachytherapy (internal radiationtherapy).

In another embodiment, the invention provides kits for diagnosing orprognosing lung cancer or characterizing the responsiveness of a subjecthaving lung cancer to treatment. In some embodiments, the kit comprisesone or more reagents for detection of miR205-5p and miR126 from asample. In some embodiments, the kit comprises one or more reagents fordetection of miR210, FUT8 and lncRNA from a sample, includingpolynucleotides comprising any or all of SEQ ID NOS:1-12 and mRQ 3′primer (Clontech, Mountain View, Calif.).

In some embodiments, a kit of the invention provides a reagent (e.g.,primers as described herein) for measuring copy numbers or expression.If desired, the kit further comprises instructions for measuring copynumber or expression and/or instructions for administering a therapy toa subject having lung cancer.

In particular embodiments, the instructions include at least one of thefollowing: description of the therapeutic agent; dosage schedule andadministration for treatment of lung cancer or symptoms thereof;precautions; warnings; indications; counter-indications; over dosageinformation; adverse reactions; animal pharmacology; clinical studies;and/or references. The instructions may be printed directly on thecontainer (when present), or as a label applied to the container, or asa separate sheet, pamphlet, card, or folder supplied in or with thecontainer.

In some embodiments, the kit comprises a sterile container whichcontains a therapeutic or diagnostic composition; such containers can beboxes, ampoules, bottles, vials, tubes, bags, pouches, blister-packs, orother suitable container forms known in the art. Such containers can bemade of plastic, glass, laminated paper, metal foil, or other materialssuitable for holding medicaments.

Application of the teachings of the present invention to a specificproblem is within the capabilities of one having ordinary skill in theart in light of the teaching contained herein. Examples of thecompositions and methods of the invention appear in the followingnon-limiting Examples.

EXAMPLES Example 1. A Classifier Integrating Plasma Biomarkers andRadiological Characteristics for Distinguishing Malignant from BenignPulmonary Nodules

Using microarray and droplet digital PCR to directly profile plasmamiRNA expressions of 135 patients with PNs, 11 plasma miRNAs wereidentified that displayed a significant difference between patients withmalignant versus benign PNs. Using multivariate logistic regressionanalysis of the molecular results and clinical/radiologicalcharacteristics, an integrated classifier was developed comprising twomiRNA biomarkers and one radiological characteristic for distinguishingmalignant from benign PNs. The classifier had 89.9% sensitivity and90.9% specificity, being significantly higher compared with thebiomarkers or clinical/radiological characteristics alone (All P<0.05).The classifier was validated in two independent sets of patients. It isshown for the first time that the integration of plasma biomarkers andradiological characteristics could more accurately identify lung canceramong indeterminate PNs. Future use of the classifier could spareindividuals with benign growths from the harmful diagnostic procedures,while allowing effective treatments to be immediately initiated for lungcancer, thereby reduces the mortality and cost.

Numerous plasma miRNA biomarkers have been searched by detectingcirculating miRNAs directly released from primary tumors or thecirculating lung cancer cells, but have limited success, due to severalchallenges (Mitchell P S, Parkin R K, Kroh E M, et al. CirculatingmicroRNAs as stable blood-based markers for cancer detection. Proc NatlAcad Sci USA, 105: 10513-8, 2008): 1), the release of contaminatingmiRNAs in plasma by hemolysis of blood cells always produces a lowspecificity with inconsistent results for cancer diagnosis. 2), sincethe amount of miRNAs directly derived from primary tumors in plasma isvery low and further ‘diluted’ in a background of normal miRNAs(Mitchell P S, Parkin R K, Kroh E M, et al. Circulating microRNAs asstable blood-based markers for cancer detection. Proc Natl Acad Sci USA,105: 10513-8, 2008), some cancer cell derived-miRNAs presenting at verylow abundance in plasma are undetectable by RT-PCR, producing a verypoor sensitivity for cancer detection (Whale A S, Huggett J F, Cowen S,et al. Comparison of microfluidic digital PCR and conventionalquantitative PCR for measuring copy number variation. Nucleic Acids Res,40: e82, 2012). 3), no standard endogenous control exists in plasma fornormalizing circulating miRNAs, resulting in poor reproducibility androbustness among different studies. To address the challenges, first, inthis study we use the EDRN-established SOPs for collecting and preparingblood specimens to reduce bias related to sampling methods, storage orpurification, and to diminish the contamination of the bloodcells-related miRNAs in plasma. Indeed, expression levels of bloodcells-related miRNAs in our samples are negative, suggesting that thereare no contaminated miRNAs from hemolysis of the blood cells. Second, wehave demonstrated that ddPCR could directly and reliably quantify lowabundance miRNAs in clinical samples with a higher sensitivity thanconventional RT-PCR (Ma J, Li N, Guarnera M, et al. Quantification ofPlasma miRNAs by Digital PCR for Cancer Diagnosis. Biomark Insights, 8:127-36, 2013; Li N, Ma J, Guarnera M A, et al. Digital PCRquantification of miRNAs in sputum for diagnosis of lung cancer. JCancer Res Clin Oncol, 140: 145-50, 2014). Furthermore, ddPCR canabsolutely quantify copy number of miRNAs. In addition, ddPCR does notrequire external calibrators or endogenous control genes. Moreover, itis relatively resistant to PCR inhibitors. In this study we use ddPCR toanalyze miRNAs in plasma. Our results show that miR-205-5p, a key lungtumor-specific miRNA presenting at a very low level unreliablydetectable by RT-PCR in plasma (Shen J, Todd N W, Zhang H, et al. PlasmamicroRNAs as potential biomarkers for non-small-cell lung cancer. LabInvest, 91: 579-87, 2011), is robustly and reproducibly quantified byddPCR. Since ddPCR could reliably and sensitively measure the vitalmiRNAs that were not previously detectable in plasma using RT-PCR, ournewly developed three miRNA biomarkers are not the same as the previousones developed by using RT-PCR (Shen J, Jiang F. Applications ofMicroRNAs in the Diagnosis and Prognosis of Lung Cancer. Expert Opin MedDiagn, 6: 197-207, 2012; Shen J, Todd N W, Zhang H, et al. PlasmamicroRNAs as potential biomarkers for non-small-cell lung cancer. LabInvest, 91: 579-87, 2011; Shen J, Stass S A, Jiang F. MicroRNAs aspotential biomarkers in human solid tumors. Cancer Lett, 329: 125-36,2013; Shen J, Liu Z, Todd N W, et al. Diagnosis of lung cancer inindividuals with solitary pulmonary nodules by plasma microRNAbiomarkers. BMC Cancer, 11: 374, 2011). However, although using onlythree miRNAs, the logistic model has a higher specificity compared witha circulating miRNA signature composed by reciprocal ratios among 24miRNAs (87% vs. 81%) for identifying lung cancer (Sozzi G, Conte D, LeonM, et al. Quantification of free circulating DNA as a diagnostic markerin lung cancer. J Clin Oncol, 21: 3902-8, 2003).

Previously, some models based on the clinical/radiological variableshave shown the potential for predicting malignant PNs (Swensen S J,Silverstein M D, Ilstrup D M, et al. The probability of malignancy insolitary pulmonary nodules. Application to small radiologicallyindeterminate nodules. Arch Intern Med, 157: 849-55, 1997, Schultz E M,Sanders G D, Trotter P R, et al. Validation of two models to estimatethe probability of malignancy in patients with solitary pulmonarynodules. Thorax, 63: 335-41, 2008; McWilliams A, Tammemagi M C, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on firstscreening CT. N Engl J Med, 369: 910-9, 2013). Our present studyconfirms the previous findings that the clinical and radiologicalvariables could be predictors for malignant PNs. However, the moderatesensitivity and specificity of these models limits the application inthe clinical settings (Swensen S J, Silverstein M D, Ilstrup D M, et al.The probability of malignancy in solitary pulmonary nodules. Applicationto small radiologically indeterminate nodules. Arch Intern Med, 157:849-55, 1997; Schultz E M, Sanders G D, Trotter P R, et al. Validationof two models to estimate the probability of malignancy in patients withsolitary pulmonary nodules. Thorax, 63: 335-41, 2008). We reason thatintegrating plasma biomarkers with radiological/clinical characteristicsmight have a synergistic effect for identifying NSCLC among theindeterminate PNs. Indeed, by integrating the plasma biomarkers withradiological characteristics, we develop a classifier for distinguishingmalignant from benign PNs. Although this simple classifier comprisesonly two biomarkers and one radiological variable of PNs, it has highersensitivity and specificity compared with the panel of biomarkers or theMayo Clinic model used alone. Furthermore, the performance of theclassifier developed in White Americans and African Americas isconfirmed in a geographically independent cohort (Chinese population),further implying the usefulness for detection of NSCLC amongindeterminate PNs. In addition, the classifier with a simple equationand a single cut-off value would offer a convenient analytic means inthe laboratory settings for the classification of malignant from benignPNs.

The areas for improvement do exist in the current study. A screeningassay directed at a malignancy with an incidence ≤5% should have asensitivity exceeding 95% when the specificity is <95%, and vice versa.The incidence of lung cancer in heavy smokers is less than 3%, whileLDCT has 90% sensitivity and only 61% specificity, producing a highfalse positive rate. Only the approaches with more than 95% specificityand appropriate sensitivity for identifying malignant PNs couldsupplement LDCT lung cancer screening. Although showing promise, ourdeveloped classifier with 89% sensitivity and 90% specificity does nothold the required performance. The two plasma miRNA biomarkers in theclassifier were developed from the limited number of miRNAs identifiedby microarrays, however, by which other important lung cancer-associatedmiRNAs might not been included. We are using high-throughputnext-generation sequencing to directly analyze plasma samples ofpatients with either malignant or benign PNs to identify new miRNAbiomarkers for lung cancer. The performance of this classifier could befurther improved by adding the new plasma miRNA biomarkers that are morespecific to malignant PNs. Furthermore, the radiological features usedin the study are obtained through the conventional image analysis thatis based on subjective observation and limited to the measurements ofnodule size in one dimension. Radiomics, an emerging techniqueextracting a large number of quantitative features from medical imagesautomatically, provides a more detailed quantification of tumorphenotypic characteristics that have diagnostic value. In the future, weintend to incorporate the molecular biomarkers, radiomic features ofnodules, and clinical characteristics of smokers to develop a classierthat could more accurately and conveniently identify lung cancer amongthe indeterminate PNs.

In summary, we have for the first time developed a simple classifier byintegrating plasma biomarkers with radiological characteristics thatcould identify lung cancer among indeterminate PNs. Future use of theclassifier by sparing individuals with benign growths from the harmfuldiagnostic procedures, while allowing effective treatments to beimmediately initiated for NSCLC, would complement LDCT for the earlydetection of lung cancer. Nevertheless, undertaking a prospective studyto further validate the classifier for lung cancer in a largepopulation-based LDCT screening positive setting among heavy smokers isrequired.

Materials and Methods Patient Cohorts and Research Design

The study protocols were approved by the Institutional Review Boards ofthe University of Maryland Medical Center (UMMC), the Baltimore VAMedical Center (BVAMC), and Jiangsu Province Hospital of TraditionalChinese Medicine (JPHTCM). Inclusion criteria were current and formersmokers who had CT-detected PNs and were between the ages of 55-74.Exclusion criteria included pregnancy or lactation, current pulmonaryinfection, thoracic surgery within 6 months, radiotherapy to the chestwithin 1 year, and life expectancy of <1 year. A PN was defined as asolitary, round, or oval lesion in the lung parenchyma in the absence ofadenopathy, atelectasis, or pneumonia. We reviewed the medical recordsfor their demographic and clinical variables about age, gender, race,ethnicity, history of cancer, and smoking behavior (smoking history,smoking status, pack years, and number of years since quitting). We alsoobtained radiographic characteristics of the PNs on CT images, includingthe maximum transverse size, the visually determined type (nonsolid orground-glass opacity, part-solid, solid, perifissural, and spiculation),and the location in the lungs. A definitive malignant diagnosis wasestablished and verified based on pathologic examination of tissuesobtained via surgery or biopsy. A definitive benign diagnosis wasestablished when a specific benign etiology was confirmedpathologically, or the PNs were clinically and radiographically stableafter a 2-year follow-up with multiple examinations (MacMahon H, AustinJ H, Gamsu G, et al. Guidelines for management of small pulmonarynodules detected on CT scans: a statement from the Fleischner Society.Radiology, 237: 395-400, 2005; Moyer V A. Screening for prostate cancer:U.S. Preventive Services Task Force recommendation statement. Ann InternMed, 157: 120-34, 2012). The surgical pathologic staging was determinedaccording to the TNM classification of the International Union AgainstCancer with the American Joint Committee on Cancer and the InternationalStaging System for Lung Cancer (Ohori M, Wheeler T M, Scardino P T. TheNew American Joint Committee on Cancer and International Union AgainstCancer TNM classification of prostate cancer. Clinicopathologiccorrelations. Cancer, 74: 104-14, 1994). Histopathologic classificationwas determined according to the World Health Organization classification(Travis W D, Brambilla E, Nicholson A G, et al. The 2015 World HealthOrganization Classification of Lung Tumors: Impact of Genetic, Clinicaland Radiologic Advances Since the 2004 Classification. J Thorac Oncol,10: 1243-60, 2015). Altogether, we recruited 135, 126, and 98 patientswith PNs from UMMC, BVAMC, and JPHTCM, respectively. Of the UMMC cohort,69 had malignant PNs and were diagnosed with NSCLC, and 66 had benignPNs (Table 1). The 66 subjects with benign PNs were diagnosed withgranulomatous inflammation (n=34), nonspecific inflammatory changes(n=23), or lung infections (n=9). From the UMMC cohort, we randomlyselected 18 individuals with malignant PNs and 18 individuals withbenign PNs, from whom, the plasma samples were analyzed by using amicroarray to identify miRNA biomarker candidates for lung cancer. Theidentified miRNAs were then validated in all 135 plasma samples of theUMMC cohort by using droplet digital PCR (ddPCR). The resulted moleculardata and clinical/radiological characteristics of the UMMC cohort ofpatients with PNs were analyzed to identify an optimal panel ofbiomarkers and then construct a classifier for identifying malignantPNs. 126 patients with PNs recruited from BVAMC were used as anindependent testing cohort, while 98 patients with PNs recruited fromJPHTCM were used an external testing cohort to confirm the classifierfor the differentiation of malignant from benign PNs. The BVAMC cohortconsisted of 63 patients with malignant PNs (NSCLC) and 63 patients withbenign PNs (Table 2). The 63 subjects with benign PNs were diagnosedwith granulomatous inflammation (n=30), nonspecific inflammatory changes(n=19), or lung infections (n=14). In the JPHTCM cohort, 49 hadmalignant PNs (NSCLC) and 49 had benign PNs. The 49 subjects with benignPNs were diagnosed with granulomatous inflammation (n=26), nonspecificinflammatory changes (n=17), or lung infections (n=6). The demographicand clinical parameters, including information about nodules size, ofthe three cohorts are shown in Tables 1-2.

TABLE 1 Characteristics of patients recruited in the University ofMaryland Medical Center Patients with Patients with malignant PNs benignPNs Characteristics (n = 69) (n = 66) Clinical Age 68.22 (SD 9.90) 65.27(SD 8.26) Sex Male 46 45 Female 23 21 Race African American 21 20 White48 46 Smoking history Current smoker 41 40 Former smoker 28 26Pack-years 43.26 (SD 13.12) 23.69 (SD 12.28) Years quit 7.16 (SD 4.69)12.69 (SD 8.27) History of cancer  7  2 Stage of non-small cell cancerStage I 18 Stage II 18 Stage III-VI 23 Histological type AC 33 SCC 29 LC 7 Radiological Nodule size (mm) 19.89 (SD 12.16) 10.18 (SD 5.55) NoduleLocation Left lower lobe  9 13 Left upper lobe 25 18 Right lower lobe 1520 Right middle lobe  4  7 Right upper lobe 15  8 Nodule type (number)Nonsolid or ground- 18 20 glass opacity Perifissural  7  9 Part-solid  911 Solid 13 14 Spiculation 22 12 Abbreviations: PN, pulmonary nodule;SD, standard deviation; AC, adenocarcinoma; SCC, squamous cellcarcinoma; LCC, large cell carcinoma.

TABLE 2 Characteristics of patients recruited in the Baltimore VAMedical Center Patients with Patients with malignant PNs benign PNsCharacteristics (n = 63) (n = 63) Clinical Age 67.38 (SD 9.16) 64.48 (SD9.01) Sex Male 43 42 Female 20 21 Race African American 19 20 White 4443 Smoking history Current smoker 40 39 Former smoker 23 24 Pack-years44.67 (SD 12.19) 25.78 (SD 13.19) Years quit 6.78 (SD 8.38) 11.15 (SD7.79) History of cancer 4  1 Stage of non-small cell cancer Stage I 20Stage II 19 Stage III-VI 24 Histological type AC 31 SCC 28 LC 4Radiological Nodule size (mm) 18.34 (SD 13.02) 10.39 (SD 6.02) NoduleLocation Left lower lobe 8 11 Left upper lobe 21 17 Right lower lobe 1520 Right middle lobe 5  7 Right upper lobe 14  8 Nodule type (number)Nonsolid or ground- 17 19 glass opacity Perifissural 7  9 Part-solid 710 Solid 12 13 Spiculation 20 12 Abbreviations: PN, pulmonary nodule;SD, standard deviation; AC, 63 adenocarcinoma; SCC, squamous cellcarcinoma; LCC, large cell carcinoma.

Blood Collection, Plasma Preparation, and RNA Isolation

The blood samples were collected before any treatment regimen.Variability in the blood collection and preparation might haveconfounding effects on the molecular analysis of the body fluidspecimens. Furthermore, qualities of RNA samples are crucial for theaccurate and robust measurement of plasma miRNAs. To reduce thevariability and bias linked to sampling methods, storage orpurification, in the three medical centers we collected blood andprepared plasma using the standard operating protocols (SOPs) developedby The National Cancer Institute Early Detection Research Network (EDRN)(Marks J R, Anderson K S, Engstrom P, et al. Construction and analysisof the NCI-EDRN breast cancer reference set for circulating markers ofdisease. Cancer Epidemiol Biomarkers Prev 2015; 24: 435-41; Tuck M K,Chan D W, Chia D, et al. Standard operating procedures for serum andplasma collection: early detection research network consensus statementstandard operating procedure integration working group. J Proteome Res2009; 8: 113-7). Furthermore, the release of contaminating miRNAs inplasma by hemolysis of blood cells such as red blood cells (RBCs) couldyield nonspecific results. To avoid the contamination, we preparedplasma from blood within 2 hours after the collection as previouslydescribed (Shen J, Todd N W, Zhang H, et al. Plasma microRNAs aspotential biomarkers for non-small-cell lung cancer. Lab Invest, 91:579-87, 2011; Ma J, Li N, Guarnera M, et al. Quantification of PlasmamiRNAs by Digital PCR for Cancer Diagnosis. Biomark Insights, 8: 127-36,2013; Shen J, Liu Z, Todd N W, et al. Diagnosis of lung cancer inindividuals with solitary pulmonary nodules by plasma microRNAbiomarkers. BMC Cancer, 11: 374, 2011. Moreover, we used RBC lysissolution to maximally reduce the possible contamination from RBCs inplasma (Shen J, Stass S A, Jiang F. MicroRNAs as potential biomarkers inhuman solid tumors. Cancer Lett, 329: 125-36, 2013; Whale A S, Huggett JF, Cowen S, et al. Comparison of microfluidic digital PCR andconventional quantitative PCR for measuring copy number variation.Nucleic Acids Res, 40: e82, 2012; Li H, Jiang Z, Leng Q, et al. Aprediction model for distinguishing lung squamous cell carcinoma fromadenocarcinoma. Oncotarget, 11: 226-32, 2017; Liu H, Zhu L, Liu B, etal. Genome-wide microRNA profiles identify miR-378 as a serum biomarkerfor early detection of gastric cancer. Cancer Lett, 316: 196-203, 2012;Schisterman E F, Perkins N J, Liu A, et al. Optimal cut-point and itscorresponding Youden Index to discriminate individuals using pooledblood samples. Epidemiology, 16: 73-81, 2005; Hanley J A, McNeil B J. Amethod of comparing the areas under receiver operating characteristiccurves derived from the same cases. Radiology, 148: 839-43, 1983; SozziG, Conte D, Leon M, et al. Quantification of free circulating DNA as adiagnostic marker in lung cancer. J Clin Oncol, 21: 3902-8, 2003). Weextracted RNA from plasma by using a protocol with miRNeasy Mini Kitspin column as described in our published work (Shen J, Jiang F.Applications of MicroRNAs in the Diagnosis and Prognosis of Lung Cancer.Expert Opin Med Diagn, 6: 197-207, 2012; Shen J, Todd N W, Zhang H, etal. Plasma microRNAs as potential biomarkers for non-small-cell lungcancer. Lab Invest, 91: 579-87, 2011; Shen J, Liao J, Guarnera M A, etal. Analysis of MicroRNAs in sputum to improve computed tomography forlung cancer diagnosis. J Thorac Oncol, 9: 33-40, 2014; Shen J, Stass SA, Jiang F. MicroRNAs as potential biomarkers in human solid tumors.Cancer Lett, 329: 125-36, 2013; Ma J, Li N, Guarnera M, et al.Quantification of Plasma miRNAs by Digital PCR for Cancer Diagnosis.Biomark Insights, 8: 127-36, 2013; Shen J, Liu Z, Todd N W, et al.Diagnosis of lung cancer in individuals with solitary pulmonary nodulesby plasma microRNA biomarkers. BMC Cancer, 11: 374, 2011; Su Y, Fang H,Jiang F. Integrating DNA methylation and microRNA biomarkers in sputumfor lung cancer detection. Clin Epigenetics, 8: 109, 2016; Su Y,Guarnera M A, Fang H, et al. Small non-coding RNA biomarkers in sputumfor lung cancer diagnosis. Mol Cancer, 15: 36, 2016; Su J, Anjuman N,Guarnera M A, et al. Analysis of Lung Flute-collected Sputum for LungCancer Diagnosis. Biomark Insights, 10: 55-61, 2015; Su J, Liao J, GaoL, et al. Analysis of small nucleolar RNAs in sputum for lung cancerdiagnosis. Oncotarget, 7: 5131-42, 2016; Xing L, Su J, Guarnera M A, etal. Sputum microRNA biomarkers for identifying lung cancer inindeterminate solitary pulmonary nodules. Clin Cancer Res, 21: 484-9,2015; Li N, Ma J, Guarnera M A, et al. Digital PCR quantification ofmiRNAs in sputum for diagnosis of lung cancer. J Cancer Res Clin Oncol,140: 145-50, 2014; Anjuman N, Li N, Guarnera M, et al. Evaluation oflung flute in sputum samples for molecular analysis of lung cancer. ClinTransl Med, 2: 15, 2013; Yu L, Todd N W, Xing L, et al. Early detectionof lung adenocarcinoma in sputum by a panel of microRNA markers. Int JCancer, 127: 2870-8, 2010; Xing L, Todd N W, Yu L, et al. Earlydetection of squamous cell lung cancer in sputum by a panel of microRNAmarkers. Mod Pathol, 23: 1157-64, 2010; Xie Y, Todd N W, Liu Z, et al.Altered miRNA expression in sputum for diagnosis of non-small cell lungcancer. Lung Cancer, 67: 170-6, 2010; Cao X, Wu Z, Jiang F, et al.Identification of chilling stress-responsive tomato microRNAs and theirtarget genes by high-throughput sequencing and degradome analysis. BMCGenomics, 15: 1130, 2014; Li P, Zhang Q, Wu X, et al. CirculatingmicroRNAs serve as novel biological markers for intracranial aneurysms.J Am Heart Assoc, 3: e000972, 2014; Huang Y, Yang S, Zhang J, et al.MicroRNAs as promising biomarkers for diagnosing human cancer. CancerInvest, 28: 670-1, 2010; Ma J, Li N, Lin Y, et al. CirculatingNeutrophil MicroRNAs as Biomarkers for the Detection of Lung Cancer.Biomark Cancer, 8: 1-7, 2016).

In addition, we analyzed expression levels of RBC-related miRNA(mir-451), myeloid-related miRNA (miR-223), and lymphoid-associatedmiRNA (miR-150) in all the RNA samples. The samples that were positiveto these blood cells-related miRNAs were excluded from the study. RNAwas immediately stored at −80 in a barcoded cryotube until use.

Microarray Analysis

The plasma RNA specimens were analyzed for miRNA expressions by using“Exiqon Services” (Exiqon, Denmark) with an established protocol asdescribed in our previous reports (Ma J, Li N, Lin Y, et al. CirculatingNeutrophil MicroRNAs as Biomarkers for the Detection of Lung Cancer.Biomark Cancer 2016; 8: 1-7; Ma J, Lin Y, Zhan M, et al. DifferentialmiRNA expressions in peripheral blood mononuclear cells for diagnosis oflung cancer. Lab Invest 2015; 95: 1197-206).

Briefly, 6 μl RNA was reversely transcribed in 30 μl reactions using themiRCURY LNA™ Universal RT miRNA PCR, Polyadenylation and cDNA synthesiskit (Exiqon). cDNA was diluted 100× and assayed in 10 ul PCR reactionsaccording to the protocol for miRCURY LNA™ Universal RT miRNA PCR. EachmiRNA was assayed by qPCR on the miRNA Ready-to-Use PCR, Haman Panels Iusing ExiLENT SYBR® Green master mix. Negative controls excludingtemplate from the reverse transcription reaction were performed andprofiled like the samples. The amplification was performed in aLightCycler® 480 Real-Time PCR System (Roche, San Francisco, Calif.) in384 well plates. The amplification curves were made by usingquantification cycle (Cq), which was used as a relative value forfurther quantification of the tested genes. We normalized the resulteddata by using the average of assays detected in the samples(average—assay Cq).

Droplet Digital PCR (ddPCR)

ddPCR analysis for quantification of miRNAs was done as described in ourpublished studies (Ma J, Li N, Guarnera M, et al. Quantification ofPlasma miRNAs by Digital PCR for Cancer Diagnosis. Biomark Insights, 8:127-36, 2013; Li N, Ma J, Guarnera M A, et al. Digital PCRquantification of miRNAs in sputum for diagnosis of lung cancer. JCancer Res Clin Oncol, 140: 145-50, 2014; Ma J, Mannoor K, Gao L, et al.Characterization of microRNA transcriptome in lung cancer bynext-generation deep sequencing. Mol Oncol, 8: 1208-19, 2014).

Briefly, 1 ul RNA per sample was obtained for RT to produce cDNA byTaqMan miRNA RT Kit (Applied Biosystems, Foster City, Calif.) andspecific primers for each gene. 20 μl reaction mixture containing 5 μlof cDNA solution, 10 μl Supermix, 1 μl of Taqman primer/probe mix wasloaded into a cartridge with droplet Generation oil (Bio-Rad, Hercules,Calif.) and then placed into the QX100 Droplet Generator (Bio-Rad). Thegenerated droplets were transferred to a 96-well PCR plate. PCRamplification was carried on a T100 thermal cycler (Bio-Rad). ddPCR wasa direct method for quantitatively measuring nucleic acids (Li N, Ma J,Guarnera M A, et al. Digital PCR quantification of miRNAs in sputum fordiagnosis of lung cancer. J Cancer Res Clin Oncol, 140: 145-50, 2014).The number of positive reactions, together with Poisson's distribution,were used to produce a straight and high-confidence measurement of theoriginal target concentration (Whale A S, Huggett J F, Cowen S, et al.Comparison of microfluidic digital PCR and conventional quantitative PCRfor measuring copy number variation. Nucleic Acids Res, 40: e82, 2012).Therefore, ddPCR could absolutely quantify targeted nucleic acidsequences without requiring external calibrators or endogenous controls(genes) (Ma J, Li N, Guarnera M, et al. Quantification of Plasma miRNAsby Digital PCR for Cancer Diagnosis. Biomark Insights, 8: 127-36, 2013;Whale A S, Huggett J F, Cowen S, et al. Comparison of microfluidicdigital PCR and conventional quantitative PCR for measuring copy numbervariation. Nucleic Acids Res, 40: e82, 2012; Li H, Jiang Z, Leng Q, etal. A prediction model for distinguishing lung squamous cell carcinomafrom adenocarcinoma. Oncotarget, 11: 226-32, 2017). The plate was loadedon Droplet Reader (Bio-Rad), by which copy number of each miRNA per μlPCR reaction mixture was directly determined. All assays were performedin triplicates. Furthermore, two interplate controls and one no-templatecontrol were carried along in each experiment. The no template controlfor RT was RNease free water instead of RNA sample input, and notemplate control for PCR was RNease free water instead of RT productsinput.

Statistical Analysis

To identify plasma miRNAs that were differentially expressed in patientswith malignant versus benign PNs, we expected the acceptable number offalse positives to be 1.0, fold difference between cases and controls at2.0, standard deviation of the gene measurements on the base-twologarithmic scale at 0.7, and desired power at 80%. Given 375 miRNAsincluded in the array, at least 15 specimens for each type of thepatients were required to achieve the statistical criteria. Furthermore,based on one-sample with binomially distributed outcomes, we needed 45cases from each group at 5% significant level with 80% power to discoverand validate a panel of biomarkers or classifier for predictingmalignant PNs. For analysis of microarray data, we performed an unpairedunequal variance t test with Benjamini-Hochberg correction (Liu H, ZhuL, Liu B, et al. Genome-wide microRNA profiles identify miR-378 as aserum biomarker for early detection of gastric cancer. Cancer Lett, 316:196-203, 2012) to identify differentially expressed miRNAs in plasma ofpatients with malignant versus benign PNs. We used univariate analysisto determine which of plasma miRNAs and clinical and radiologicalvariables were associated with malignant PNs. The significantlyassociated factors were then analyzed by using multivariate logisticregression models with constrained parameters as in least absoluteshrinkage and selection operator (LASSO) based on receiver-operatorcharacteristic (ROC) curve to identify an optimal panel of miRNAbiomarkers and construct a classifier for malignant PNs. The optimalcutoff value was generated using the Youden index (Schisterman E F,Perkins N J, Liu A, et al. Optimal cut-point and its correspondingYouden Index to discriminate individuals using pooled blood samples.Epidemiology, 16: 73-81, 2005). The 95% confidence intervals in the ROCplot for proportions were estimated. To compare the performance of theclassifier with that of the plasma biomarkers and the Mayo Clinic model(Swensen S J, Silverstein M D, Ilstrup D M, et al. The probability ofmalignancy in solitary pulmonary nodules. Application to smallradiologically indeterminate nodules. Arch Intern Med, 157: 849-55,1997), we used the method of Hanley and McNeil with the area under anROC curve (AUC) analysis (Hanley J A, McNeil B J. A method of comparingthe areas under receiver operating characteristic curves derived fromthe same cases. Radiology, 148: 839-43, 1983). The classifier wasblindly validated in two additional sets of patients by comparing thecalculated results with final clinical diagnosis and the AUCs.

Results

Identifying Differentially-Expressed miRNAs in Plasma of Patients withMalignant Versus Benign PNs

Of the 375 miRNAs embodied on the miRNA array, 282 (75.2%) showed a <35Cq value in all plasma specimens of 36 patients with either malignant(18) or benign (18) PNs. Furthermore, the miRNA expression levelsmeasured by using the microarray in the replicates of each sample werehighly correlated (all p<0.0001). Therefore, the 282 miRNAs werereliably measurable in plasma of the patients with PNs. Among themiRNAs, 11 (miRs-21-5p, -103a-3p, -126-3p, -135a-5p, -145-5p, -141-3p,-193b-3p, -200b-3p, -205-5p, -210, and -301b) exhibited more than 2.0fold-changes with a p<0.05 in plasma of patients with malignant versusbenign PNs (Table 13). Of the 11 miRNAs, nine (miRs-21-5p, -103a-3p,-126, 141-3p, -193b-3p, -205-5p, 210, and -301b) had a higher expressionlevel, whereas three (miRs-135a-5p, 145-5p, and -200b-3p) displayed alower level in plasma of patients with malignant versus benign PNs (AllP<0.05).

Validating the Changes of the Plasma miRNAs, and Developing miRNABiomarkers for Malignant PNs

The changes of 11 malignant PN-related plasma miRNAs identified by themicroarray should be validated using a different and reliable technique(Shen J, Stass S A, Jiang F. MicroRNAs as potential biomarkers in humansolid tumors. Cancer Lett, 329: 125-36, 2013). We demonstrated thatddPCR was a more sensitive technique with greater precision andreproducibility to detect expression of miRNAs in plasma than did theconventional reverse transcription PCR (RT-PCR) (Ma J, Li N, Guarnera M,et al. Quantification of Plasma miRNAs by Digital PCR for CancerDiagnosis. Biomark Insights, 8: 127-36, 2013; Li N, Ma J, Guarnera M A,et al. Digital PCR quantification of miRNAs in sputum for diagnosis oflung cancer. J Cancer Res Clin Oncol, 140: 145-50, 2014). Moreover,ddPCR could absolutely and precisely determine copy number of miRNAswithout the need of an internal control gene, such as U6. Therefore, weused ddPCR to assess changes of the 11 miRNAs in 135 plasma samples ofthe UMMC cohort. Each well of the plasma samples contained at least10,000 droplets. By contrast, no product was synthesized in the negativecontrol samples. Thus, the plasma samples were successfully “read” forthe absolute quantification of the 11 miRNAs by using a reliable andaccurate assay. ddPCR analysis showed that all the 11 miRNAs displayed asignificantly different level in plasma samples of patients withmalignant PNs versus individuals with benign diseases (all P<0.05).Furthermore, the miRNAs had consistent changes detected by ddPCR in thesame direction as in the microarray analysis: nine displayed a higherexpression level, whereas three exhibited a lower level in plasma ofsubjects with malignant versus benign PNs (Table 3).

TABLE 3 Expression levels of 11 plasma miRNAs and their diagnosticsignificance in 135 patients with malignant versus benign PNs Mean (SD)in Mean (SD) in patients with patients with AUC (95% miRNAs benign PNsmalignant PNs P-value confidence interval) miR-21-5p 8.9893 (5.7922)18.9694 (23.4001) 0.0458 0.6135 (0.5102 to 0.7168) miR-103a-3p 1.5987(1.9266)  7.0492 (14.2826) 0.0386 0.6160 (0.5155 to 0.7166) miR-126-3p4.6113 (4.5983) 20.1927 (40.4664) 0.0301 0.6075 (0.4977 to 0.7173)miR-135a-5p 0.0237 (0.0305) 0.0123 (0.0180) 0.0068 0.5696 (0.4784 to0.6608) miR-145-5p 0.9343 (1.4553) 0.7990 (1.4933) 0.0004 0.6761 (0.5868to 0.7655) miR-141-3p 0.0239 (0.0663) 0.0481 (0.1143) 0.0480 0.5962(0.5064 to 0.6861) miR-193b-3p 0.0523 (0.0525) 0.0772 (0.0810) 0.02030.6318 (0.5468 to 0.7168) miR-200b-3p 0.0319 (0.0453) 0.0151 (0.0190)0.0010 0.6199 (0.5330 to 0.7069) miR-205-5p 0.0114 (0.0191) 0.0672(0.1054) <0.001 0.8187 (0.7503 to 0.8871) miR-210 0.3579 (0.6268) 0.8951(1.2892) 0.0054 0.6525 (0.5527 to 0.7523) miR-301b 0.2491 (0.2926)0.9524 (1.8623) 0.0130 0.6115 (0.5201 to 0.7029) Abbreviations: PN,pulmonary nodules; SD, standard deviation; AUC, the area under receiveroperating characteristic curve.

To determine the diagnostic values of the plasma miRNAs, ROC and theAUCs were calculated by using the copy number of each miRNA in the UMMCcohort of 135 patients. The individual miRNAs exhibited AUC values of0.57-0.82 in distinguishing malignant from benign PNs (Table 3). We usedlogistic regression models with constrained parameters as in LASSO basedon ROC criterion to identify and optimize a panel of miRNA biomarkers.The three miRNAs, miRs-126, 210, and 205-5p are selected as the bestbiomarkers (all P<0.001). Combined use of the three miRNAs produced 0.87AUC for distinguishing malignant from benign PNs. Furthermore, Pearsoncorrelation analysis indicated that the estimated correlation amongexpression levels of the three miRNAs in plasma was low (All P>0.05),implying that the diagnostic values of the miRNAs were complementary toeach other. Subsequently, combined use of the three miRNAs generated asensitivity of 81.2% and a specificity of 86.4% (FIG. 1A). Moreover,including other miRNAs in the model did not improve the accuracy for theidentification of malignant PNs. The logistic model had no statisticallysignificant association with histological type and stage of the NSCLC,and patients' age, gender, ethnicity, and smoking history (All p>0.05).

Developing a Classifier by Integrating the Biomarkers and RadiographicFeatures of PNs for Identifying Malignant PNs

Although showing promise, the 81.2% sensitivity and 86.4% specificity ofthe three miRNA biomarkers used together are not sufficient in theclinic for distinguishing malignant from benign PNs. We used univariateanalysis to determine which of clinical and radiological variables wereassociated with malignant PNs in 135 patients of the UMMC cohort.History of cancer and smoking pack-years of the patients, the diameter,spiculation, and upper lobe location of the PNs were associated withmalignant PNs (Table 4).

TABLE 4 Association of clinical and radiological variables withmalignant PNs Variables p-value Age (year) 0.1729 Sex 0.7388 Race 0.9648Current smoker 0.2295 Former smoker 0.6548 Smoking package-year <0.0001Cancer history <0.0001 Nodule diameter on CT <0.0001 Nodule spiculationon CT <0.0001 Year after quit 0.0126 Upper lobe locations of PNs 0.0065Abbreviations: PN, pulmonary nodule.

We then used logistic regression models with constrained parameters asin LASSO based on ROC criterion to eliminate the large number of theparameters to construct a classifier including: miRs-205-5p and 126, anddiameter of PN. The classifier had 0.95 AUC in distinguishing malignantfrom benign PNs (FIG. 1B). Adding other miRNAs and imaging/clinicalvariables in the classifier did not improve the performance forpredicting malignant PNs. Furthermore, prediction models based onparameters of PN on CT images and clinical characteristics of smokershave been developed for predicting the probability of malignant PNs(Swensen S J, Silverstein M D, Ilstrup D M, et al. The probability ofmalignancy in solitary pulmonary nodules. Application to smallradiologically indeterminate nodules. Arch Intern Med, 157: 849-55,1997; Gould M K, Ananth L, Barnett P G. A clinical model to estimate thepretest probability of lung cancer in patients with solitary pulmonarynodules. Chest, 131: 383-8, 2007; Schultz E M, Sanders G D, Trotter P R,et al. Validation of two models to estimate the probability ofmalignancy in patients with solitary pulmonary nodules. Thorax, 63:335-41, 2008; McWilliams A, Tammemagi M C, Mayo J R, et al. Probabilityof cancer in pulmonary nodules detected on first screening CT. N Engl JMed, 369: 910-9, 2013), of which, the Mayo Clinic model is a commonlyused one. We applied the Mayo Clinic model (Swensen S J, Silverstein MD, Ilstrup D M, et al. The probability of malignancy in solitarypulmonary nodules. Application to small radiologically indeterminatenodules. Arch Intern Med, 157: 849-55, 1997) in the same set of 135patients for predicting lung cancer by using the equation. The MayoClinic model produced an AUC of 0.82 (FIG. 1C), a similar value as shownin the previous reports (Swensen S J, Silverstein M D, Ilstrup D M, etal. The probability of malignancy in solitary pulmonary nodules.Application to small radiologically indeterminate nodules. Arch InternMed, 157: 849-55, 1997; Gould M K, Ananth L, Barnett P G. A clinicalmodel to estimate the pretest probability of lung cancer in patientswith solitary pulmonary nodules. Chest, 131: 383-8, 2007; Schultz E M,Sanders G D, Trotter P R, et al. Validation of two models to estimatethe probability of malignancy in patients with solitary pulmonarynodules. Thorax, 63: 335-41, 2008). The direct comparison showed thatthe classifier had a significantly higher AUC value (0.95) compared withthe Mayo Clinic model (0.82) and the panel of three biomarkers (0.87)used alone (All P<0.05) in the same set of patients (FIG. 1A-C). As aresult, our classifier yielded 89.9% sensitivity and 90.9% specificityfor diagnosis of malignant PNs, which were also significantly highercompared with those of the Mayo Clinic model (75.4% sensitivity and80.3% specificity) and the biomarkers (81.2% sensitivity and 86.4%specificity) (all P<0.05) (FIG. 1A-C). Moreover, the classifier did notexhibit statistical differences of sensitivity and specificity betweenhistological types and stages of NSCLC (P>0.05).

Validating the Classifier for Differentiating Malignant from Benign PNsin Two Independent Cohorts of Patients with PNs

The three miRNAs (miRs-126, 210, and 205-5p) were analyzed for theexpression levels in plasma of both BVAMC and JPHTCM cohorts. The threemiRNAs showed a similar change pattern in the two independent cohorts asin the UMMC cohort, providing additional evidence that the plasma miRNAscould be reproducibly measured. The classifier produced 0.94 AUC with asensitivity of 88.9% and a specificity of 90.5% for diagnosis ofmalignant PNs in BVAMC cohort (Table 5). The classifier produced highersensitivity and specificity than did the panel of the biomarkers (80.9%sensitivity and 85.7% specificity) and the Mayo Clinic model (74.6%sensitivity and 79.4% specificity) (All P<0.05) (Table 5). Theclassifier was further validated in an external set of 98 patients withPNs (the JPHTCM cohort) recruited in China for the diagnostic value in ablinded fashion. The classifier created an AUC of 0.94 with asensitivity of 87.8% and a specificity of 89.8% for detection ofmalignant PNs. Furthermore, the classifier had higher sensitivity andspecificity (87.8% sensitivity and 89.8% specificity) for detection ofmalignant PNs than did the panel of the biomarkers (81.6% sensitivityand 85.7% specificity) and the Mayo Clinic model (73.5% sensitivity and75.5% specificity) (Table 5) (All P<0.05). Taken together, the resultscreated from the extensive validations confirmed the potential of theclassifier for estimating the probability of lung cancer amongindeterminate PNs.

TABLE 5 Comparison of the classifier, panel of the three plasma miRNAbiomarkers, and Mayo Clinic model for distinguishing malignant frombenign PNs in two independent cohorts of patients* BVAMC patients JPHTCMApproaches Sensitivity (95% CI) Specificity (95% CI) Sensitivity (95%CI) Specificity (95% CI) The classifier 88.89% 90.48% 87.76% 89.80%(78.44% to (80.41% to (75.23% to (77.77% to 95.41%) 96.42%) 95.37%)96.60%) The biomarker panel 80.95% 85.71% 81.63% 85.71% (69.09% to(74.61% to (67.98% to (72.76% to 89.75%) 93.25%) 91.24%) 94.06%) TheMayo Clinic model 74.60% 79.37% 73.47% 75.51% (62.06% to (67.30% to(58.92% to (61.13% to 84.73%) 88.53%) 85.05%) 86.66%) Abbreviations:BVAMC, Baltimore VA Medical Center patients; JPHTCM, Jiangsu ProvinceHospital of Traditional Chinese Medicine; CI, confidence interval. *AllP values < 0.05.

Example 2. A Plasma Long Non-Coding RNA Signature for Early Detection ofLung Cancer

By using droplet digital PCR, we determined the diagnostic performanceof 26 lung cancer-associated lncRNAs in plasma of a development cohortof 63 lung cancer patients and 33 cancer-free individuals, and avalidation cohort of 39 lung cancer patients and 22 controls. In thedevelopment cohort, seven of the 26 lncRNAs were reliably measured inplasma. Two (SNHG1 and RMRP) displayed a considerably high plasma levelin lung cancer patients vs. cancer-free controls (all P<0.001). Combineduse of the plasma lncRNAs as a biomarker signature produced 84.13%sensitivity and 87.88% specificity for diagnosis of lung cancer,independent of stage and histological type of lung tumor, and patients'age and sex (all p >0.05). The diagnostic value of the plasma lncRNAsignature for lung cancer early detection was confirmed in thevalidation cohort. The plasma lncRNA signature may provide a potentialblood-based assay for diagnosing lung cancer at the early stage.Nevertheless, a prospective study is warranted to validate its clinicalvalue.

Circulating cell-free lncRNAs biomarkers show promise as biomarkers forcancer diagnosis. However, unlike other ncRNA (e.g., microRNAs), lncRNAshave the lowest levels in plasma among several different RNA species(Schlosser, K., Hanson, J., Villeneuve, P. J., Dimitroulakos, J.,McIntyre, L., Pilote, L., and Stewart, D. J. (2016) Sci Rep 6, 36596),presenting a major challenge for the development of cell-free lncRNAbiomarkers. Schlosser et al recently demonstrated that expressions oflncRNAs were robustly detectable in tissues, however, undetectable orsporadically measurable in the matched plasma by using qRT-PCR, aroutine platform used for nucleic acid detection 64. Regular qPCR hassome limitations in determining expression of ncRNAs: i), it is anindirect and labor-consuming approach. ii), it requires an internalcontrol gene for normalization. Yet none of the investigated genes hasbeen accepted as a standard control. iii), its sensitivity for a lowcopy number of genes is very low. Our current observations areconsistent with Schlosser's finding (Schlosser, K., Hanson, J.,Villeneuve, P. J., Dimitroulakos, J., McIntyre, L., Pilote, L., andStewart, D. J. (2016) Sci Rep 6, 36596). Of the 26 lungcancer-associated lncRNAs, none is reliably measurable in plasma usingqRT-PCR, when a CT of 35 is used as the cut off value. Therefore,conventional qPCR might not be an appropriate tool for the developmentof lncRNAs as circulating biomarkers, given that circulating lncRNAs inbody fluids are present in low abundance. We have shown that ddPCR is adirect method for absolutely and quantitatively measuring ncRNAs (Ma,J., Li, N., Guarnera, M., and Jiang, F. (2013) Biomark Insights 8,127-136; Li, N., Ma, J., Guarnera, M. A., Fang, H., Cai, L., and Jiang,F. (2014) J Cancer Res Clin Oncol 140, 145-150; Li, H., Jiang, Z., Leng,Q., Bai, F., Wang, J., Ding, X., Li, Y., Zhang, X., Fang, H., Yfantis,H. G., Xing, L., and Jiang, F. (2017) Oncotarget 8, 50704-50714), sinceit depends on limiting partition of the PCR volume, where a positiveresult of a large number of microreactions indicates the presence of asingle molecule in a given reaction (Day, E., Dear, P. H., andMcCaughan, F. (2013) Methods 59, 101-107). The number of positivereactions, together with Poisson's distribution produces a straight andhigh-confidence measurement of the original target concentration.Importantly, ddPCR does not require a reliance on rate-basedmeasurements (CT values), endogenous controls, and calibration curves,and therefore overcome the obstacles linked to the regular qPCR inquantification of genes in plasma. Here we demonstrate that seven of the26 lung cancer-associated lncRNAs that are not detectable by qRT-PCR arerobustly measurable by ddPCR in plasma. Therefore, ddPCR may address thelimitations of the qPCR in quantification of lncRNAs in plasma, andhence help develop cell-free cancer biomarkers.

The previous plasma lncRNA-based assays were mostly developed from thelimited number of lung cancer-associated lncRNAs and only consisted of asingle lncRNA gene (Liang, W., Lv, T., Shi, X., Liu, H., Zhu, Q., Zeng,J., Yang, W., Yin, J., and Song, Y. (2016) Medicine (Baltimore) 95,e4608; Tantai, J., Hu, D., Yang, Y., and Geng, J. (2015) Int J Clin ExpPathol 8, 7887-7895; Li, N., Feng, X. B., Tan, Q., Luo, P., Jing, W.,Zhu, M., Liang, C., Tu, J., and Ning, Y. (2017) Dis Markers 2017,7439698; Li, N., Wang, Y., Liu, X., Luo, P., Jing, W., Zhu, M., and Tu,J. (2017) Technol Cancer Res Treat, 1533034617723754; Wan, L., Zhang,L., Fan, K., and Wang, J. J. (2017) Onco Targets Ther 10, 5695-5702;Tan, Q., Zuo, J., Qiu, S., Yu, Y., Zhou, H., Li, N., Wang, H., Liang,C., Yu, M., and Tu, J. (2017) Int J Oncol 50, 1729-1738; Zhu, Q., Lv,T., Wu, Y., Shi, X., Liu, H., and Song, Y. (2017) J Cell Mol Med 21,2184-2198; Zhu, H., Zhang, L., Yan, S., Li, W., Cui, J., Zhu, M., Xia,N., Yang, Y., Yuan, J., Chen, X., Luo, J., Chen, R., Xing, R., Lu, Y.,and Wu, N. (2017) Oncotarget 8, 7867-7877; Wang, H. M., Lu, J. H., Chen,W. Y., and Gu, A. Q. (2015) Int J Clin Exp Med 8, 11824-11830).

Since lung tumor is a heterogeneous group of neoplasms and develops froma multitude of molecular changes, a single lncRNA-based assay may notachieve the performance required to move forward for clinicallydetecting lung cancer. The development of a panel of multiple biomarkersby integrating analysis of multifaceted and diverse lncRNAs wouldprovide a synergistic test for lung cancer diagnosis. By searchingpublished data, we found 21 lncRNAs whose malfunction was wellcharacterized in lung tumorigenesis (Li, M., Qiu, M., Xu, Y., Mao, Q.,Wang, J., Dong, G., Xia, W., Yin, R., and Xu, L. (2015) Tumour Biol 36,9969-9978; Wei, M. M., Zhou, Y. C., Wen, Z. S., Zhou, B., Huang, Y. C.,Wang, G. Z., Zhao, X. C., Pan, H. L., Qu, L. W., Zhang, J., Zhang, C.,Cheng, X., and Zhou, G. B. (2016) Oncotarget 7, 59556-59571; Shen, L.,Chen, L., Wang, Y., Jiang, X., Xia, H., and Zhuang, Z. (2015) JNeurooncol 121, 101-108; Loewen, G., Jayawickramarajah, J., Zhuo, Y.,and Shan, B. (2014) J Hematol Oncol 7, 90; Li, P., Li, J., Yang, R.,Zhang, F., Wang, H., Chu, H., Lu, Y., Dun, S., Wang, Y., Zang, W., Du,Y., Chen, X., Zhao, G., and Zhang, G. (2015) Diagn Pathol 10, 63; Yang,Y. R., Zang, S. Z., Zhong, C. L., Li, Y. X., Zhao, S. S., and Feng, X.J. (2014) Int J Clin Exp Pathol 7, 6929-6935; Whiteside, E. J., Seim,I., Pauli, J. P., O'Keeffe, A. J., Thomas, P. B., Carter, S. L.,Walpole, C. M., Fung, J. N., Josh, P., Herington, A. C., and Chopin, L.K. (2013) Int J Oncol 43, 566-574; Hu, T., and Lu, Y. R. (2015) CancerCell Int 15, 36; Li, J., Li, P., Zhao, W., Yang, R., Chen, S., Bai, Y.,Dun, S., Chen, X., Du, Y., Wang, Y., Zang, W., Zhao, G., and Zhang, G.(2015) Cancer Cell Int 15, 48; Zeng, Z., Bo, H., Gong, Z., Lian, Y., Li,X., Zhang, W., Deng, H., Zhou, M., Peng, S., Li, G., and Xiong, W.(2016) Tumour Biol 37, 729-737; Hou, Z., Zhao, W., Zhou, J., Shen, L.,Zhan, P., Xu, C., Chang, C., Bi, H., Zou, J., Yao, X., Huang, R., Yu,L., and Yan, J. (2014) Int J Biochem Cell Biol 53, 380-388; Luo, J.,Tang, L., Zhang, J., Ni, J., Zhang, H. P., Zhang, L., Xu, J. F., andZheng, D. (2014) Tumour Biol 35, 11541-11549; Luo, H., Sun, Y., Wei, G.,Luo, J., Yang, X., Liu, W., Guo, M., and Chen, R. (2015) Biochemistry54, 2895-2902; Wu, Y., Liu, H., Shi, X., Yao, Y., Yang, W., and Song, Y.(2015) Oncotarget 6, 9160-9172; Qiu, M., Xu, Y., Yang, X., Wang, J., Hu,J., Xu, L., and Yin, R. (2014) Tumour Biol 35, 5375-5380; Qiu, M., Xu,Y., Wang, J., Zhang, E., Sun, M., Zheng, Y., Li, M., Xia, W., Feng, D.,Yin, R., and Xu, L. (2015) Cell Death Dis 6, e1858; Zhang, L., Zhou, X.F., Pan, G. F., and Zhao, J. P. (2014) Biomed Pharmacother 68, 401-407;Nie, F. Q., Sun, M., Yang, J. S., Xie, M., Xu, T. P., Xia, R., Liu, Y.W., Liu, X. H., Zhang, E. B., Lu, K. H., and Shu, Y. Q. (2015) MolCancer Ther 14, 268-277; Yang, R., Li, P., Zhang, G., Lu, C., Wang, H.,and Zhao, G. (2017) Cell Physiol Biochem 42, 126-136; Sang, H., Liu, H.,Xiong, P., and Zhu, M. (2015) Tumour Biol 36, 4027-4037; Shi, X., Sun,M., Liu, H., Yao, Y., Kong, R., Chen, F., and Song, Y. (2015) MolCarcinog 54 Suppl 1, E1-E12; Han, L., Kong, R., Yin, D. D., Zhang, E.B., Xu, T. P., De, W., and Shu, Y. Q. (2013) Med Oncol 30, 694; Han, L.,Zhang, E. B., Yin, D. D., Kong, R., Xu, T. P., Chen, W. M., Xia, R.,Shu, Y. Q., and De, W. (2015) Cell Death Dis 6, e1665; Xie, X., Liu, H.T., Mei, J., Ding, F. B., Xiao, H. B., Hu, F. Q., Hu, R., and Wang, M.S. (2014) Int J Clin Exp Pathol 7, 8881-8886; Liu, J., Wan, L., Lu, K.,Sun, M., Pan, X., Zhang, P., Lu, B., Liu, G., and Wang, Z. (2015) PLoSOne 10, e0114586; Sun, M., Liu, X. H., Wang, K. M., Nie, F. Q., Kong,R., Yang, J. S., Xia, R., Xu, T. P., Jin, F. Y., Liu, Z. J., Chen, J.F., Zhang, E. B., De, W., and Wang, Z. X. (2014) Mol Cancer 13, 68;Yang, Y., Li, H., Hou, S., Hu, B., Liu, J., and Wang, J. (2013) PLoS One8, e65309).

Furthermore, by systematically and comprehensively define ncRNA changesof NSCLC in surgical lung tumor tissues using whole-transcriptome NGS(Ma, J., Mannoor, K., Gao, L., Tan, A., Guarnera, M. A., Zhan, M.,Shetty, A., Stass, S. A., Xing, L., and Jiang, F. (2014) Mol Oncol 8,1208-1219; Gao, L., Ma, J., Mannoor, K., Guarnera, M. A., Shetty, A.,Zhan, M., Xing, L., Stass, S. A., and Jiang, F. (2015) Int J Cancer 136,E623-629), we recently identified additional five lung cancer-associatedlncRNAs (Ma, J., Mannoor, K., Gao, L., Tan, A., Guarnera, M. A., Zhan,M., Shetty, A., Stass, S. A., Xing, L., and Jiang, F. (2014) Mol Oncol8, 1208-1219; Gao, L., Ma, J., Mannoor, K., Guarnera, M. A., Shetty, A.,Zhan, M., Xing, L., Stass, S. A., and Jiang, F. (2014) Int J Cancer).

Both the published and our NGS-defied lncRNAs of lung tumors may providea comprehensive set of high-quality biomarker candidates for lungcancer. From the 26 lncRNAs, our present study identified and optimizeda plasma signature consisting of two lncRNAs that created a higherdiagnostic value for lung cancer detection than did individual lncRNAsused alone (Liang, W., Lv, T., Shi, X., Liu, H., Zhu, Q., Zeng, J.,Yang, W., Yin, J., and Song, Y. (2016) Medicine (Baltimore) 95, e4608;Tantai, J., Hu, D., Yang, Y., and Geng, J. (2015) Int J Clin Exp Pathol8, 7887-7895; Li, N., Feng, X. B., Tan, Q., Luo, P., Jing, W., Zhu, M.,Liang, C., Tu, J., and Ning, Y. (2017) Dis Markers 2017, 7439698; Li,N., Wang, Y., Liu, X., Luo, P., Jing, W., Zhu, M., and Tu, J. (2017)Technol Cancer Res Treat, 1533034617723754; Wan, L., Zhang, L., Fan, K.,and Wang, J. J. (2017) Onco Targets Ther 10, 5695-5702; Tan, Q., Zuo,J., Qiu, S., Yu, Y., Zhou, H., Li, N., Wang, H., Liang, C., Yu, M., andTu, J. (2017) Int J Oncol 50, 1729-1738; Zhu, Q., Lv, T., Wu, Y., Shi,X., Liu, H., and Song, Y. (2017) J Cell Mol Med 21, 2184-2198; Zhu, H.,Zhang, L., Yan, S., Li, W., Cui, J., Zhu, M., Xia, N., Yang, Y., Yuan,J., Chen, X., Luo, J., Chen, R., Xing, R., Lu, Y., and Wu, N. (2017)Oncotarget 8, 7867-7877; Wang, H. M., Lu, J. H., Chen, W. Y., and Gu, A.Q. (2015) Int J Clin Exp Med 8, 11824-11830).

In addition, the diagnostic performance of the biomarkers was furtherblindly validated in a different cohort, suggesting that the plasmasignature might be a robust assay for lung cancer diagnosis. Moreover,the performance of this plasma lncRNA signature for lung cancerdiagnosis was independent of tumor stage and histology. This might be animportant characteristic if the plasma lncRNA signature is employed foridentifying early stage lung cancer.

The two lncRNAs (SNHG1 and RMRP) have diverse and important functions inlung tumorigenesis through regulating different molecular pathways.Elevated expression of SNHG1 was frequently observed in lung cancertissues and significantly correlated with larger tumor size, advancedstage, lymph node metastasis and poor overall survival of the patients(Cui, Y., Zhang, F., Zhu, C., Geng, L., Tian, T., and Liu, H. (2017)Oncotarget 8, 17785-17794). Furthermore, SNHG1 could promote NSCLCprogression of lung cancer via miR-101-3p/SOX9/Wnt/β-catenin regulatorynetwork and miR-145-5p/MTDH axis (Lu, Q., Shan, S., Li, Y., Zhu, D.,Jin, W., and Ren, T. (2018) FASEB J, fj201701237RR; Cui, Y., Zhang, F.,Zhu, C., Geng, L., Tian, T., and Liu, H. (2017) Oncotarget 8,17785-17794). In addition, SNHG1 plays an oncogenic role in lungsquamous cell carcinoma through ZEB1 signaling pathway by inhibitingTAp63 (Zhang, H. Y., Yang, W., Zheng, F. S., Wang, Y. B., and Lu, J. B.(2017) Biomed Pharmacother 90, 650-658). RMRP is best known for being acomponent of the nuclear RNase MRP complex, which participates in theprocessing of ribosomal RNA to generate the short mature 5.8S rRNA(Schmitt, M. E., and Clayton, D. A. (1993) Mol Cell Biol 13, 7935-7941)and cleaves B-cyclin mRNA, lowering B-cyclin levels during mitosis (Noh,J. H., Kim, K. M., Abdelmohsen, K., Yoon, J. H., Panda, A. C., Munk, R.,Kim, J., Curtis, J., Moad, C. A., Wohler, C. M., Indig, F. E., de Paula,W., Dudekula, D. B., De, S., Piao, Y., Yang, X., Martindale, J. L., deCabo, R., and Gorospe, M. (2016) Genes Dev 30, 1224-1239). In addition,RMRP interacts with telomerase to form a complex with RNA-dependent RNApolymerase activity capable of synthesizing dsRNA precursors processedby DICER1 into siRNAs (Maida, Y., Yasukawa, M., Furuuchi, M., Lassmann,T., Possemato, R., Okamoto, N., Kasim, V., Hayashizaki, Y., Hahn, W. C.,and Masutomi, K. (2009) Nature 461, 230-235). Moreover, RMRP isimportant for mitochondrial DNA replication and RNA processing (Chang,D. D., and Clayton, D. A. (1987) Science 235, 1178-1184). Upregulationof RMRP is found in lung adenocarcinoma tissues (Meng, Q., Ren, M., Li,Y., and Song, X. (2016) PLoS One 11, e0164845). RMRP might act as anoncogenic lncRNA to promote the expression of KRAS, FMNL2 and SOX9 byinhibiting miR-206 expression in lung cancer (Meng, Q., Ren, M., Li, Y.,and Song, X. (2016) PLoS One 11, e0164845). Our current study extendsthe previous findings by developing them as a biomarker signature thatmight be clinically useful in the early detection of lung cancer.

Materials and Methods Patients and Clinical Specimens

This study was approved by the Institutional Review Boards of Universityof Maryland Baltimore and Veterans Affairs Maryland Health Care System.We recruited lung cancer patients and cancer-free smokers by using theinclusion and/or exclusion criteria recommended by U.S. PreventiveServices Task Force for lung cancer screening in heavy smokers(Humphrey, L. L., Deffebach, M., Pappas, M., Baumann, C., Artis, K.,Mitchell, J. P., Zakher, B., Fu, R., and Slatore, C. G. (2013) AnnIntern Med 159, 411-420). We collected blood in BD Vacutainerspray-coated K2EDTA Tubes (BD, Franklin Lakes, N.J.) and prepared plasmausing the standard operating protocols developed by The NCI-EarlyDetection Research Network (Marks, J. R., Anderson, K. S., Engstrom, P.,Godwin, A. K., Esserman, L. J., Longton, G., Iversen, E. S., Mathew, A.,Patriotis, C., and Pepe, M. S. (2015) Cancer Epidemiol Biomarkers Prev24, 435-441; Tuck, M. K., Chan, D. W., Chia, D., Godwin, A. K., Grizzle,W. E., Krueger, K. E., Rom, W., Sanda, M., Sorbara, L., Stass, S., Wang,W., and Brenner, D. E. (2009) J Proteome Res 8, 113-117). The specimenswere processed within 2 hours of collection by centrifugation at 1,300×gfor 10 minutes at 4° C. A total of 102 NSCLC patients and 55 cancer-freesmokers were recruited. Among the cancer patients, 24 patients werefemale and 78 were male. Twenty-three had stage I NSCLC, 18 with stageII, 28 with stage III, 28 with stage IV, and 5 with unknown stage. Ofthe cancer-free smokers, 14 patients were female and 41 were male. Therewere no significant differences of age, gender and smoking statusbetween the NSCLC patients and cancer-free smokers. The cases andcontrols were randomly grouped into two cohorts: a development cohortand a validation cohort. The development cohort consisted of 63 lungcancer patients and 33 cancer-free smokers, while the validation cohortcomprised 39 lung cancer patients and 22 cancer-free smokers. Thedemographic and clinical variables of the two cohorts are shown inTables 6-7.

RNA Isolation and Quantitative Reverse Transcriptase PCR (qRT-PCR)

RNA was extracted from the specimens by using Trizol LS reagent(Invitrogen Carlsbad, Calif.) and RNeasy Mini Kit (Qiagen, Hilden,Germany) (Ma, J., Jemal, A., and Smith, R. (2013) Cancer 119, 3420-3421;Shen, J., Liu, Z., Todd, N. W., Zhang, H., Liao, J., Yu, L., Guarnera,M. A., Li, R., Cai, L., Zhan, M., and Jiang, F. (2011) BMC Cancer 11,374; Shen, J., Todd, N. W., Zhang, H., Yu, L., Lingxiao, X., Mei, Y.,Guarnera, M., Liao, J., Chou, A., Lu, C. L., Jiang, Z., Fang, H., Katz,R. L., and Jiang, F. (2011) Lab Invest 91, 579-587). RT was carried outto generate cDNA by using a RT Kit (Applied Biosystems, Foster City,Calif.) as described in our published works (Ma, J., Jemal, A., andSmith, R. (2013) Cancer 119, 3420-3421; Shen, J., Liu, Z., Todd, N. W.,Zhang, H., Liao, J., Yu, L., Guarnera, M. A., Li, R., Cai, L., Zhan, M.,and Jiang, F. (2011) BMC Cancer 11, 374; Shen, J., Todd, N. W., Zhang,H., Yu, L., Lingxiao, X., Mei, Y., Guarnera, M., Liao, J., Chou, A., Lu,C. L., Jiang, Z., Fang, H., Katz, R. L., and Jiang, F. (2011) Lab Invest91, 579-587). PCR was performed to measure expressions of target genesby using a PCR kit (Applied Biosystems) on a Bio-Red IQ5 Multi-colorRT-PCR Detection System (Bio-Red, Hercules, Calif.). Expression levelsof the genes were determined using comparative cycle threshold (CT)method with miR-1228 as an internal control. The targeted genes with CTvalues >35 were considered to be below the detection level of qRT-PCR(Guthrie, J. L., Seah, C., Brown, S., Tang, P., Jamieson, F., and Drews,S. J. (2008) J Clin Microbiol 46, 3798-3799).

Droplet Digital PCR

ddPCR for analysis of expression level of the genes was performed asdescribed in our previous work (Li, N., Ma, J., Guarnera, M. A., Fang,H., Cai, L., and Jiang, F. (2014) J Cancer Res Clin Oncol 140, 145-150;Ma, J., Li, N., Guarnera, M., and Jiang, F. (2013) Biomark Insights 8,127-136). Briefly, TaqMan™ reaction mix (Applied Biosystems) containingsample cDNA was partitioned into aqueous droplets in oil via the QX100Droplet Generator (Bio-Rad), and then transferred to a 96-well PCRplate. A two-step thermocycling protocol (95° C.×10 min; 40 cycles of[94° C.×30 s, 60° C.×60 s], 98° C.×10 min) was undertaken in a Bio-RadC1000 (Bio-Rad). The PCR plate was loaded on Droplet Reader (Bio-Rad),by which copy number of each gene per μl PCR reaction was directlydetermined. We used QuantaSoft 1.7.4 analysis software (Bio-Rad) andPoisson statistics to compute droplet concentrations (copies/μL). Onlygenes that had at least 10,000 droplets were considered to be robustlydetectable by ddPCR in plasma and subsequently underwent furtheranalysis (Ma, J., Li, N., Guarnera, M., and Jiang, F. (2013) BiomarkInsights 8, 127-136; Li, N., Ma, J., Guarnera, M. A., Fang, H., Cai, L.,and Jiang, F. (2014) J Cancer Res Clin Oncol 140, 145-150). All assayswere done in triplicates, and one no-template control and two interplatecontrols were carried along in each experiment.

Statistical Analysis

Pearson's correlation analysis was applied to assess relationshipbetween gene expressions and demographic and clinical characteristics ofthe lung cancer patients and control individuals. The area underreceiver operating characteristic (ROC) curve (AUC) analyses were usedto determine sensitivity, specificity, and corresponding cut-off valueof each gene (Dodd, L. E., and Pepe, M. S. (2003) Biometrics 59,614-623). All P values shown were two sided, and a P value of <0.05 wasconsidered statistically significant.

Results

Developing a Plasma lncRNA Signature for Lung Cancer Early Detection

We first measured expression levels of the 26 lncRNAs in plasma by usingqRT-PCR in a discovery cohort of 63 cases and 33 controls. The lncRNAshad a CT value of ≥35 in 75% plasma samples. However, the internalcontrol gene, miR-1228, stably displayed a CT value of 20-22 across theplasma samples. The results suggested that the amplification curves forthe lncRNAs were not reliably generated, and their expression levels inplasma were too low to be detectable by qRT-PCR. We have proven thatddPCR is a direct method for absolutely and quantitatively measuringncRNAs with a higher sensitivity compared with qRT-PCR (Ma, J., Li, N.,Guarnera, M., and Jiang, F. (2013) Biomark Insights 8, 127-136; Li, N.,Ma, J., Guarnera, M. A., Fang, H., Cai, L., and Jiang, F. (2014) JCancer Res Clin Oncol 140, 145-150; Li, H., Jiang, Z., Leng, Q., Bai,F., Wang, J., Ding, X., Li, Y., Zhang, X., Fang, H., Yfantis, H. G.,Xing, L., and Jiang, F. (2017) Oncotarget 8, 50704-50714). Therefore, weused ddPCR to determine expression level of the lncRNAs in the plasmasamples. Seven (26.9%) of the 26 lncRNAs could generated at least 10,000droplets in each well of the plasma samples. Therefore, the sevenlncRNAs could be successfully “read” by ddPCR for the absolutequantification in the plasma samples. The seven genes are SNHG1, MALAT1,HOTAIR, H19, MEG3, MEG8, and RMRP.

TABLE 6 Characteristics of NSCLC patients and cancer- free smokers in adevelopment cohort NSCLC cases Controls P- (n = 63) (n = 33) value Age67.93 (SD9.16) 63.79 (SD 16.12) 0.18 Sex 0.36 Female 15 8 Male 48 25Smoking pack-years (median) 32.1 31.76 0.19 Stage Stage I 14 Stage II 10Stage III 17 Stage IV 18 Unknown 4 Histological type

TABLE 7 Characteristics of NSCLC patients and cancer- free smokers in avalidation cohort NSCLC cases Controls P- (n = 39) (n = 22) value Age66.58 (SD 9.93) 63.68 (SD 13.27) 0.25 Sex 0.45 Female 9 6 Male 30 16Smoking pack-years 33.39 29.64 0.26 (median) Stage Stage I 9 Stage II 8Stage III 11 Stage IV 10 Unknown 1 Histological type Adenocarcinoma 22Squamous cell carcinoma 17 Abbreviations: NSCLC, non-small cell lungcancer.

Of the seven genes, SNHG1 and RMRP had an elevated plasma level in lungcancer patients vs. cancer-free controls (All p<0.05) (FIG. 2A), beingconsistent with those in primary lung tumor tissues (Cui, Y., Zhang, F.,Zhu, C., Geng, L., Tian, T., and Liu, H. (2017) Oncotarget 8,17785-17794; Meng, Q., Ren, M., Li, Y., and Song, X. (2016) PLoS One 11,e0164845). Therefore, the level of the two lncRNAs in plasma mightreflect those in the tumors of the lung cancer patients. However, otherfive lncRNAs did not exhibit a different plasma level in lung cancercases vs. controls (All p>0.05). Furthermore, SNHG1 and RMRP exhibitedAUC values of 0.90 and 0.80, respectively, in distinguishing NSCLCpatients from the healthy individuals (FIG. 2B). Using Youden's index(Schisterman, E. F., Perkins, N. J., Liu, A., and Bondell, H. (2005)Epidemiology 16, 73-81), we set up optimal cutoff for the two genes at1.11 and 0.12, respectively. As a result, the use of the individualgenes alone produced 61.00-78.78% sensitivities and 87.88-90.91% (Table8). Combined use of the two genes based on at least one positive resultin either SNHG1 or RMRP produced the highest classification accuracy(85.42%) compared to any one used alone (all p<0.05) (Table 8). The twogenes used in combination produced a sensitivity of 84.13% and aspecificity of 87.88% for diagnosis of lung cancer, thus considerablyimproving the detection rate by a single gene with only a 2% decline inspecificity (Table 8). Furthermore, the estimated correlation determinedby Pearson's correlation analysis among levels of the two lncRNAs wasvery low (R2=0.011, p=0.53), further supporting that the combinedanalysis of the two genes outperformed a single one. In addition,combined analysis of the 2 plasma biomarkers did not show specialassociation with stage and histological type of lung cancer, andpatients' age, gender, and smoking status (All P>0.05).

TABLE 8 Diagnostic performance of one-gene and vs. a plasma lncRNAsignature for lung cancer diagnosis in a development cohort. AccuracySensitivity (95% CI) Specificity (95% CI) SNHG1 81.25% 77.78% 87.88%(72.00% to (65.54% to (71.80% to 88.49%) 87.28%) 96.60%) RMRP 71.88%61.90% 90.91% (61.78% to (48.80% to (75.67% to 80.58%) 73.85%) 98.08%) Aplasma 85.42% 84.13% 87.50% lncRNA (76.74% to (72.74% to (71.80% tosignature 91.79%) 92.12%) 96.60%) Abbreviations: CI, confidenceinterval.Validating the Plasma lncRNA Marker Signature in an Independent Set ofLung Cancer Patients and Controls

To evaluate the diagnostic performance of the biomarker signature, thelncRNAs (SNHG1 and RMRP) were assessed by using ddPCR in plasma ofadditional 39 NSCLC patients and 22 healthy controls. The two genes usedin combination could differentiate the NSCLC patients from healthycontrols with 82.05% sensitivity and 83.36% specificity (Table 9).

TABLE 9 Diagnostic performance of one-gene and vs. a plasma lncRNAsignature for lung cancer diagnosis in a validation set AccuracySensitivity (95% CI) Specificity (95% CI) SNHG1 80.33% 76.92% 86.36%(68.16% to (60.67% to (65.09% to 89.40%) 88.87) 97.09%) RMRP 72.13%61.54% 90.91% (59.17% to (44.62% to (70.84% to 82.85%) 76.64%) 98.88%) Aplasma 83.62% 82.05% 86.36% lncRNA (71.91% to (66.47% to (65.09% tosignature 91.85%) 92.46%) 97.09%) Abbreviations: CI, confidenceinterval.

Furthermore, no statistically significant difference was found in thesensitivity and specificity of the biomarker signature for stages andhistological types of NSCLC (All p>0.05). Moreover, there was noassociation of expressions of the two genes with the age, gender, orsmoking status of the lung cancer patients and normal individuals (Allp >0.05). Taken together, the results confirm the potential of using thetwo lncRNAs as a plasma biomarker signature for the early detection oflung cancer.

Example 3. Fucosylation Genes as Circulating Biomarkers for Lung Cancer

Here we investigated whether transcriptional levels of genes coding theFUTs in plasma could provide cell-free circulating biomarkers for lungcancer. mRNA expression of all 13 Futs (Fut1-11, Pofut1, and Pofut2) wasevaluated in lung tumor tissues and the matched noncancerous lungtissues and plasma of 64 lung cancer patients and 32 cancer-freeindividuals by PCR assay. The developed plasma Fut biomarkers werevalidated in an independent cohort of 40 lung cancer patients and 20controls for their diagnostic performance.

Four of the 13 Futs showed a different transcriptional level in 48 lungtumor tissues compared with the matched nonconscious tissues (All<0.05). Two (Fut8, and Pofut1) of the four Futs had a higher plasmalevel in lung cancer patients compared with control subjects, andconsistent with that in lung tissue specimens. Combined analysis of thetwo Futs produced 81% sensitivity and 86% specificity for diagnosis oflung cancer, and was independent of stage and histology of lung tumors.The diagnostic performance of the two plasma biomarkers was successfullyvalidated in the different cohort of lung cancer patients and controlindividuals. The fucosylation genes may provide new circulatingbiomarkers for the early detection of lung cancer.

In this present study, we have for the first time demonstrated that mRNAexpression of Futs could provide cell-free circulating biomarkers forlung cancer. Furthermore, using ddPCR assay, a sensitive and robusttechnique, we successfully developed 2 Futs as small panel biomarkersfor effective diagnosis of lung cancer. In addition, the diagnosticperformance is independent of stage and histological type of the NSCLC,and age and gender of subjects. Therefore, the plasma biomarkers have animportant characteristic if it is employed for more precisely and easilyidentifying early stage lung cancer.

Chen et al. showed that Fut8 was up-regulated in lung cancer and tissues(Chen C Y, Jan Y H, Juan Y H, Yang C J, Huang M S, Yu C J, Yang P C,Hsiao M, Hsu T L, Wong C H: Fucosyltransferase 8 as a functionalregulator of nonsmall cell lung cancer. Proc Natl Acad Sci USA 2013,110(2):630-635). A high protein expression of FUT8 was correlated withtumor metastasis, disease recurrence, and poor survival in patients withNSCLC (Honma R, Kinoshita I, Miyoshi E, Tomaru U, Matsuno Y, Shimizu Y,Takeuchi S, Kobayashi Y, Kaga K, Taniguchi N et al: Expression offucosyltransferase 8 is associated with an unfavorable clinical outcomein non-small cell lung cancers. Oncology 2015, 88(5):298-308; Chen C Y,Jan Y H, Juan Y H, Yang C J, Huang M S, Yu C J, Yang P C, Hsiao M, Hsu TL, Wong C H: Fucosyltransferase 8 as a functional regulator of nonsmallcell lung cancer. Proc Natl Acad Sci USA 2013, 110(2):630-635).Downregulation of Fut8 significantly inhibited the malignant behaviorsof lung cancer cells. Fut8 could globally modify surface antigens,receptors, and adhesion molecules (Chen C Y, Jan Y H, Juan Y H, Yang CJ, Huang M S, Yu C J, Yang P C, Hsiao M, Hsu T L, Wong C H:Fucosyltransferase 8 as a functional regulator of nonsmall cell lungcancer. Proc Natl Acad Sci USA 2013, 110(2):630-635). Dysregulation ofFut8 was involved in the regulation of dozens of genes associated withthe malignancy through multiple mechanisms (Chen C Y, Jan Y H, Juan Y H,Yang C J, Huang M S, Yu C J, Yang P C, Hsiao M, Hsu T L, Wong C H:Fucosyltransferase 8 as a functional regulator of nonsmall cell lungcancer. Proc Natl Acad Sci USA 2013, 110(2):630-635). The observationsin the present study provide further evidence that dysregulation of FUT8has important role in lung tumorigenesis. Importantly, we demonstratethat a high mRNA expression level of Fut8 in plasma may provide usefulbiomarker for lung cancer early detection. POFUT1 is an important0-glycosyltransferase and has an essential role for extracellular Fringeto function (Stahl M, Uemura K, Ge C, Shi S, Tashima Y, Stanley P: Rolesof Pofut1 and O-fucose in mammalian Notch signaling. J Biol Chem 2008,283(20):13638-13651; Loriol C, Audfray A, Dupuy F, Germot A, Maftah A:The two N-glycans present on bovine Pofut1 are differently involved inits solubility and activity. FEBS J 2007, 274(5):1202-1211). POFUT1 canactive Notch1 in breast cancer (Wan G, Tian L, Yu Y, Li F, Wang X, Li C,Deng S, Yu X, Cai X, Zuo Z et al: Overexpression of Pofut1 and activatedNotch1 may be associated with poor prognosis in breast cancer. BiochemBiophys Res Commun 2017, 491(1):104-111). Analysis of POFUT1 may havediagnostic or prognostic value in the patients with cancers (Wan G, TianL, Yu Y, Li F, Wang X, Li C, Deng S, Yu X, Cai X, Zuo Z et al:Overexpression of Pofut1 and activated Notch1 may be associated withpoor prognosis in breast cancer. Biochem Biophys Res Commun 2017,491(1):104-111; Dong S, Wang Z, Huang B, Zhang J, Ge Y, Fan Q:Bioinformatics insight into glycosyltransferase gene expression ingastric cancer: POFUT1 is a potential biomarker. Biochem Biophys ResCommun 2017, 483(1):171-177). Ma et al. found that POFUT1 overexpressioncould prompt the binding of Notch ligand Dll1 to Notch1 receptor, andthus activated Notch1 signaling pathway in hepatocellular carcinomacells (Ma L, Dong P, Liu L, Gao Q, Duan M, Zhang S, Chen S, Xue R, WangX: Overexpression of protein O-fucosyltransferase 1 accelerateshepatocellular carcinoma progression via the Notch signaling pathway.Biochem Biophys Res Commun 2016, 473(2):503-510). Here we report anelevated mRNA level of Pofut1 in both lung tumor tissues and plasmspecimens of lung cancer patients, suggesting that the gene play animportant biological function in lung carcinogenesis. Nevertheless, thepossible biological role of aberrant expression of Fut8 and Pofut1 inlung cancer development and progression is warranted to be investigated.

Materials and Methods Patients and Clinical Specimens

This study was approved by the Institutional Review Boards of Universityof Maryland Baltimore and Veterans Affairs Maryland Health Care System.Surgically resected tissue specimens were obtained from 46 lung cancerpatients who had either a lobectomy or a pneumonectomy. Tumor tissueswere intraoperatively dissected from the surrounding lung parenchyma.The paired normal lung tissues were also obtained from the same patientsat an area distant from their tumors. Serial cryostat sections from thespecimens were prepared and used to confirm the diagnosis based on theWHO classification of tumors of the lung 10. All 48 cases were diagnosedwith histologically confirmed stage I NSCLC, including 25 AC and 23 SCC.

To collect plasma samples, we recruited lung cancer patients andcancer-free smokers by using the inclusion and/or exclusion criteriarecommended by U.S. Preventive Services Task Force for lung cancerscreening in heavy smokers (Humphrey L L, Deffebach M, Pappas M, BaumannC, Artis K, Mitchell J P, Zakher B, Fu R, Slatore C G: Screening forlung cancer with low-dose computed tomography: a systematic review toupdate the U S Preventive services task force recommendation. Ann InternMed 2013, 159(6):411-420). We collected blood in B D Vacutainerspray-coated K2EDTA Tubes (B D, Franklin Lakes, N.J.) and preparedplasma using the standard operating protocols (SOPs) developed by TheNCI-Early Detection Research Network (EDRN) (Marks J R, Anderson K S,Engstrom P, Godwin A K, Esserman L J, Longton G, Iversen E S, Mathew A,Patriotis C, Pepe M S: Construction and analysis of the NCI-EDRN breastcancer reference set for circulating markers of disease. CancerEpidemiol Biomarkers Prev 2015, 24(2):435-441; Tuck M K, Chan D W, ChiaD, Godwin A K, Grizzle W E, Krueger K E, Rom W, Sanda M, Sorbara L,Stass S et al: Standard operating procedures for serum and plasmacollection: early detection research network consensus statementstandard operating procedure integration working group. J Proteome Res2009, 8(1):113-117). The specimens were processed within 2 hours ofcollection by centrifugation at 1,300×g for 10 minutes at 4° C. A totalof 104 NSCLC patients and 52 cancer-free smokers were recruited. Amongthe cancer patients, 26 patients were female and 78 were male.Twenty-five had stage I NSCLC, 17 with stage II, 28 with stage III, 29with stage IV, and 5 with unknown stage. Of the cancer-free smokers, 13patients were female and 39 were male. There were no significantdifferences of age, gender and smoking status between the NSCLC patientsand cancer-free smokers. In this study, the cases and controls wererandomly grouped into two cohorts: a development cohort and a validationcohort. The development cohort consisted of 64 lung cancer patients and32 cancer-free smokers, while the validation cohort comprised 40 lungcancer patients and 20 cancer-free smokers. The demographic and clinicalvariables of the two sets are shown in Tables 10-11.

TABLE 10 Characteristics of NSCLC patients and cancer-free smokers in atraining set NSCLC cases Controls P- (n = 64) (n = 32) value Age 66.98(SD 9.08) 62.66 (SD 15.04) 0.14 Sex 0.33 Female 16 8 Male 48 24 Smokingpack-years 33.19 30.25 0.16 (median) Stage Stage I 15 Stage II 9 StageIII 16 Stage IV 19 Unknown 5 Histological type Adenocarcinoma 32Squamous cell carcinoma 32 Abbreviations: NSCLC, non-small cell lungcancer.

TABLE 11 Characteristics of NSCLC patients and cancer-free smokers in atesting set NSCLC cases Controls P- (n = 40) (n = 20) value Age 67.38(SD 9.17) 62.66 (SD 13.54) 0.23 Sex 0.46 Female 10 5 Male 30 15 Smokingpack-years 32.64 29.28 0.23 (median) Stage Stage I 10 Stage II 8 StageIII 12 Stage IV 10 Histological type Adenocarcinoma 22 Squamous cellcarcinoma 18 Abbreviations: NSCLC, non-small cell lung cancer.

RNA Isolation and Quantitative Reverse Transcriptase PCR

RNA was extracted from the specimens by using Trizol LS reagent(Invitrogen Carlsbad, Calif.) and RNeasy Mini Kit (Qiagen, Hilden,Germany) (Ma J, Jemal A, Smith R: Reply to lung cancer deaths averted byscreening should be considered in the context of tobacco controlpolicies. Cancer 2013, 119(18):3420-3421; Shen J, Liu Z, Todd N W, ZhangH, Liao J, Yu L, Guarnera M A, Li R, Cai L, Zhan M et al: Diagnosis oflung cancer in individuals with solitary pulmonary nodules by plasmamicroRNA biomarkers. BMC Cancer 2011, 11:374; Shen J, Todd N W, Zhang H,Yu L, Lingxiao X, Mei Y, Guarnera M, Liao J, Chou A, Lu C L et al:Plasma microRNAs as potential biomarkers for non-small-cell lung cancer.Lab Invest 2011, 91(4):579-587). RT was carried out to generate cDNA byusing a RT Kit (Applied Biosystems, Foster City, Calif.) as described inour published works (Ma J, Jemal A, Smith R: Reply to lung cancer deathsaverted by screening should be considered in the context of tobaccocontrol policies. Cancer 2013, 119(18):3420-3421; Shen J, Liu Z, Todd NW, Zhang H, Liao J, Yu L, Guarnera M A, Li R, Cai L, Zhan M et al:Diagnosis of lung cancer in individuals with solitary pulmonary nodulesby plasma microRNA biomarkers. BMC Cancer 2011, 11:374; Shen J, Todd NW, Zhang H, Yu L, Lingxiao X, Mei Y, Guarnera M, Liao J, Chou A, Lu C Let al: Plasma microRNAs as potential biomarkers for non-small-cell lungcancer. Lab Invest 2011, 91(4):579-587). PCR was performed to measureexpressions of target genes by using a PCR kit (Applied Biosystems) on aBio-Red IQ5 Multi-color RT-PCR Detection System (Bio-Red, Hercules,Calif.). Primers and probes of the targeted 13 FUTs genes are shown inthe Supplementary Table 3. Expression levels of the genes weredetermined using comparative cycle threshold (Ct) method with theequation 2-ΔΔCt by using miR-1228 as an internal control (Shen J, Liu Z,Todd N W, Zhang H, Liao J, Yu L, Guarnera M A, Li R, Cai L, Zhan M etal: Diagnosis of lung cancer in individuals with solitary pulmonarynodules by plasma microRNA biomarkers. BMC Cancer 2011, 11:374; Shen J,Todd N W, Zhang H, Yu L, Lingxiao X, Mei Y, Guarnera M, Liao J, Chou A,Lu C L et al: Plasma microRNAs as potential biomarkers fornon-small-cell lung cancer. Lab Invest 2011, 91(4):579-587; Lin Y, LengQ, Jiang Z, Guarnera M A, Zhou Y, Chen X, Wang H, Zhou W, Cai L, Fang Het al: A classifier integrating plasma biomarkers and radiologicalcharacteristics for distinguishing malignant from benign pulmonarynodules. Int J Cancer 2017, 141(6):1240-1248; Ma J, Mannoor K, Gao L,Tan A, Guarnera M A, Zhan M, Shetty A, Stass S A, Xing L, Jiang F:Characterization of microRNA transcriptome in lung cancer bynext-generation deep sequencing. Mol Oncol 2014, 8(7):1208-1219; Xing L,Todd N W, Yu L, Fang H, Jiang F: Early detection of squamous cell lungcancer in sputum by a panel of microRNA markers. Mod Pathol 2010,23(8):1157-1164; Benz F, Roderburg C, Vargas Cardenas D, Vucur M,Gautheron J, Koch A, Zimmermann H, Janssen J, Nieuwenhuijsen L, Luedde Met al: U6 is unsuitable for normalization of serum miRNA levels inpatients with sepsis or liver fibrosis. Exp Mol Med 2013, 45:e42). Thetargeted genes with Ct values >35 were considered to be below thedetection level of qRT-PCR (Guthrie J L, Seah C, Brown S, Tang P,Jamieson F, Drews S J: Use of Bordetella pertussis BP3385 to establish acutoff value for an IS481-targeted real-time PCR assay. J Clin Microbiol2008, 46(11):3798-3799).

Droplet Digital PCR

ddPCR for analysis of expression level of the genes was performed asdescribed in our previous work (Li N, Ma J, Guarnera M A, Fang H, Cai L,Jiang F: Digital PCR quantification of miRNAs in sputum for diagnosis oflung cancer. J Cancer Res Clin Oncol 2014, 140(1):145-150; Ma J, Li N,Guarnera M, Jiang F: Quantification of Plasma miRNAs by Digital PCR forCancer Diagnosis. Biomark Insights 2013, 8:127-136). Briefly, TaqMan™reaction mix (Applied Biosystems) containing sample cDNA was partitionedinto aqueous droplets in oil via the QX100 Droplet Generator (Bio-Rad),and then transferred to a 96-well PCR plate. A two-step thermocyclingprotocol (95° C.×10 min; 40 cycles of [94° C.×30 s, 60° C.×60 s], 98°C.×10 min) was undertaken in a Bio-Rad C1000 (Bio-Rad). The PCR platewas loaded on Droplet Reader (Bio-Rad), by which copy number of eachgene per μl PCR reaction was directly determined. We used QuantaSoft1.7.4 analysis software (Bio-Rad) and Poisson statistics to computedroplet concentrations (copies/μL). All assays were done in triplicates,and one no-template control and two interplate controls were carriedalong in each experiment.

Statistical Analysis

Pearson's correlation analysis was applied to assess relationshipbetween gene expressions and demographic and clinical characteristics ofthe lung cancer patients and control individuals. The area underreceiver operating characteristic (ROC) curve (AUC) analyses were usedto determine sensitivity, specificity, and corresponding cut-off valueof each gene (Dodd L E, Pepe M S: Partial AUC estimation and regression.Biometrics 2003, 59(3):614-623). All P values shown were two sided, anda P value of <0.05 was considered statistically significant.

Results

Identifying Futs Whose Abnormal Transcriptional Levels were Associatedwith Lung Cancer

The transcription levels of all 13 Futs were examined by using qRT-PCRin 48 stage I NSCLC tissues and the matched noncancerous lung tissues.Three (Futs-7 and 8, and pofut1) displayed a higher, whereas one gen(Fut-4) exhibited a lower mRNA expression levels in lung cancer tissuescompared with the matched noncancerous lung tissues (All p<0.05) (FIG. 3). There was no significant difference of the expression of the Futsbetween histological types of lung cancer (All p>0.05).

Developing a Panel of Plasma Fut Biomarkers for Lung Cancer EarlyDetection

We measured transcriptional levels of the 4 Futs in plasma by usingqRT-PCR in a training set of 64 cases and 32 controls by qRT-PCR. Ctvalue of qPCR for the 4 Futs in the plasma samples was more than 35. Theamplification curves of the RT-PCR analysis for the genes were notreliably generated. Therefore, the expression levels of the 4 lungtumor-associated Futs in plasma was too low to be detectable by qRT-PCR.We have previously demonstrated that ddPCR is a direct method forabsolutely and quantitatively measuring nucleic acids. Furthermore,ddPCR does not require a reliance on rate-based measurements (CTvalues), endogenous controls, and calibration curves. In addition, ddPCRneeds much less RNA compared with RT-PCR, and is particularly useful inthe quantification of the genes that have endogenous low-levelexpression in plasma samples. We, therefore, used ddPCR to determineexpression level of the 4 Futs in the plasma samples. Each well of thesamples contained at least 10,000 droplets. By contrast, no product wassynthesized in the negative control samples. Thus, the plasma sampleswere successfully “read” by ddPCR for the absolute quantification of the4 Fut genes.

Of the 4 Futs, Fut8 and Pofut1 had a high expression level in plasma oflung cancer patients vs. cancer-free controls (All P<0.05) (FIG. 4A).Pearson's correlation analysis showed that there were significantcorrelations between the expression levels of the 2 genes in plasma andthose in the surgical tissue specimens (All r >0.92, all P<0.05).Therefore, the level of Fut8 and Pofut1 in plasma might reflect those inthe tumors of the lung cancer patients. However, other 2 genes (Fut4 andFut8) did not display a different plasma expression in lung cancer casesvs. controls. Furthermore, Fut8 and Pofut1 exhibited AUC values of 0.86and 0.81, respectively, in distinguishing NSCLC patients from thehealthy individuals (FIG. 4B). Using Youden's index (Schisterman E F,Perkins N J, Liu A, Bondell H: Optimal cut-point and its correspondingYouden Index to discriminate individuals using pooled blood samples.Epidemiology 2005, 16(1):73-81)³², we set up optimal cutoff for the twoFUTs at 1.56 and 1.64, respectively. As a result, the use of theindividual genes alone produced 62.50-73.44% sensitivities and90.63-90.88% specificities (Table 12).

TABLE 12 Diagnostic performance of one-gene and vs. combined use of the2 genes for lung cancer diagnosis in a training set. AccuracySensitivity (95% CI) Specificity (95% CI) Fut8 79.17 73.44 90.88 (69.67to (60.20 to (75.38 to 86.79) 83.56) 97.56) Pofut1 71.88 62.50 90.63(61.78 to (49.87 to (75.39 to 80.58) 79.29) 97.22) Combined use 83.3381.76 86.26 of the 2 genes (74.33 to (77.46 to (79.20 to 91.16) 90.23)92.49) Abbreviations: CI, confidence interval.

Combined use of the two genes based on at least one positive result ineither Fut8 or Pofut1 produced the highest classification accuracy(0.833) compared to any one used alone (all p<0.05) (Table 12). The 2genes used in combination created a sensitivity of 81.76% and aspecificity of 86.26% for diagnosis of lung cancer, thus significantlyimproving the cancer detection rate by a single gene with only a 4%decrease in specificity (Table 12). Furthermore, the estimatedcorrelation determined by Pearson correlation analysis among levels ofthe 2 genes was very low (r=0.187, p=0.06), further supporting that thecombined analysis of the 2 FUT genes outperformed a single one fordiagnosis of NSCLC. In addition, combined analysis of the 2 plasmabiomarkers did not show special association with stage and histologicaltype of lung cancer, and patients' age, gender, and smoking status (AllP>0.05).

Validating the Sputum Markers in an Independent Set of Lung CancerPatients and Controls

To evaluate the diagnostic performance of the plasma biomarker panel,the 2 genes (Fut8 and Pofut1) were assessed in plasma samples of 40NSCLC patients and 20 healthy controls. The 2 genes used in combinationcould differentiate the NSCLC patients from healthy controls with 82.50%sensitivity and 85.00% specificity. Furthermore, no statisticallysignificant difference was found in the sensitivity and specificity ofthe markers for stages and histological types of NSCLC (All p>0.05).Moreover, there was no association of expressions of the 2 genes withthe age, gender, or smoking status of the lung cancer patients andnormal individuals (All p >0.05). Taken together, the results confirmthat the panel of 2 plasma biomarkers could be used for the earlydetection of lung cancer.

Example 4. Increased Sensitivity and Specificity Signature of3-Integromic Plasma Markers

Lung cancer is a heterogeneous disease and develops from a multitude ofmolecular changes. miRNAs, lncRNAs, and glycosylation genes have diversefunctions in carcinogenesis via different biological mechanisms. Usingplasma of 66 lung cancer patients and 38 controls, we investigate ifintegrating the different biomarker types could have a synergisticeffect.

We recently identified a panel of 2 plasma long non-coding RNAs(lncRNAs) biomarkers for lung cancer. Long non-coding RNAs (lncRNAs)(>200 bp) play critical and diverse regulatory roles in lungtumorigenesis through different molecular mechanisms from those ofmiRNAs. In plasma samples of 32 cancer-free subjects and 64 lung cancerpatients, we used ddPCR to evaluate the 36 lncRNAs of lung tumors thatwere characterized by our NGS analysis (FIG. 8 ).

We identified fucosylation genes as novel plasma biomarkers for lungcancer. Dysregulation of fucosylation plays an important role in lungcarcinogenesis. We used ddPCR to measure RNA expression of all 13 FUTgenes in plasma of 60 lung cancer patients and 30 cancer-free smokers,FUT8 and POFUT1 had a high expression level in lung cancer cases vs.controls. We identified a panel of 2 glycosylation genes as novel plasmabiomarkers for lung cancer (FIG. 6 ).

From the marker panels, 3 genes were selected as a 3-integromic plasmamarker signature. The probability of a lung cancer patient wascalculated using:p=−7.29+2.8*log(SNHG1)+3.83*log(FUT8)+3.36*log(miR-210). The3-integromic plasma marker signature had a greater AUC (0.97) withhigher sensitivity (94.8%) and specificity (95.6%) than did the singletype of biomarker (Table 13, FIG. 7 ). The performance is independent ofstage and histology of lung cancer, and patients' age, sex, andethnicity.

TABLE 13 Performances of the 3-integromic plasma signature and 3different panels of biomarkers Biomarkers Sensitivity (95% CI)Specificity (95% CI) 3-integromic 94.8% 95.6% signature (91.3% to (89.0%to 99.7%) 99.9%) 3 plasma miRNAs 81.2% 86.2% (75.7% to (80.4% to 93.7%)94.9%) 2 plasma lncRNAs 84.8% 88.4% (77.7% to (80.4% to 93.7%) 94.9%) 2plasma FUTs 81.8% 86.5% (74.6% to (79.3% to 87.6%) 91.9%)

While the present teachings are described in conjunction with variousembodiments, it is not intended that the present teachings be limited tosuch embodiments. On the contrary, the present teachings encompassvarious alternatives, modifications, and equivalents, as will beappreciated by those of skill in the art.

Throughout this disclosure, various publications, patents and publishedpatent specifications are referenced by an identifying citation. Thedisclosures of these publications, patents and published patentspecifications are hereby incorporated by reference into the presentdisclosure to more fully describe the state of the art to which thisinvention pertains.

1-27. (canceled)
 28. A method for predicting the presence of non-smallcell lung cancer in a subject, comprising a. obtaining the results of anassay that measures an expression level of miR-210 in a plasma samplefrom the subject; b. obtaining the results of an assay that measures anexpression level of FUT8 in a plasma sample from the subject; c.obtaining the results of an assay that measures an expression level ofSNHG1; d. calculating a probability value based on the combination ofthe expression levels of miR-210, FUT8, and SNHG1, wherein if theprobability value exceeds a specified threshold, the subject ispredicted to have lung cancer.
 29. The method of claim 28, whereincalculating the probability of lung cancer comprises generating areceiver operating characteristic (ROC) curve and calculating an areaunder the ROC curve (AUC), said area under the curve (AUC) providing theprobability of lung cancer in the subject.
 30. The method of claim 28,wherein the expression level of miR210, FUT8 and/or SNHG1 is detected byquantitative RT-PCR.
 31. The method of claim 28, wherein the expressionlevel of the miR210, FUT8 and/or SNHG1 is determined without the need ofan internal control gene.
 32. The method of claim 28, wherein theexpression level of miR210, FUT8 and/or SNHG1 is detected by dropletdigital PCR.
 33. The method of claim 28, wherein the subject is acurrent smoker.
 34. The method of claim 28, wherein the subject is aformer smoker.
 35. The method of claim 28, wherein the subject has asmoking history selected from the group consisting of at least 15pack-years, at least 20 pack-years, at least 25 pack-years at least 30pack-years, at least 35 pack-years, at least 40 pack-years, at least 45pack-years, at least 50 pack-years, at least 55 pack-years, at least 60pack-years, and at least 65 pack-years.
 36. The method of claim 28,wherein the subject is between 55 and 80 years old.
 37. The method ofclaim 28, wherein the method further comprises assaying the plasmasample for contaminating miRNA.
 38. The method of claim 37, wherein thecontaminating miRNA is selected from the group consisting of RBC-relatedmiRNA (mir-451), myeloid-related miRNA (miR-223), lymphoid-associatedmiRNA (miR-150) and combinations thereof.
 39. The method of claim 28,wherein the non-small cell lung cancer comprises an adenocarcinoma,squamous cell carcinoma or large cell carcinoma.
 40. The method of claim28, wherein miR-210 comprises SEQ ID NO:3.
 41. The method of claim 28,wherein FUT8 comprises SEQ ID NO:4.
 42. The method of claim 28, whereinSNHG1 comprises SEQ ID NO:5.
 43. The method of claim 28, wherein theprobability value is calculated by a classifier having the followingformula:probability value=−7.29+2.8×log(copy number of SNHG1/μl plasmasample)+3.83×log(copy number of FUT8/μl plasma sample)+3.36×log(copynumber of miR-210/μl plasma sample). 44-55. (canceled)